Imamura, F. et al.
Paper:
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia Fumihiko Imamura∗1 , Abdul Muhari∗1 , Erick Mas∗1 , Mulyo Harris Pradono∗2 , Joachim Post∗3 , and Megumi Sugimoto∗4 ∗1 Disaster
Control Research Center, Tohoku University Aoba 06-6-11-1106, Sendai 980-8579, Japan Email:
[email protected] ∗2 The Agency for the Assessment and Application of Technology Jl. MH, Thamrin 8, Jakarta 10340, Indonesia ∗3 German Remote Sensing Data Center (DFD), German Aerospace Center (DLR) 82234 Wessling, Germany ∗4 Earthquake Research Institute, The University of Tokyo 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan [Received September 15, 2011; accepted December 16, 2011]
This paper describes the results of a comprehensive analysis for tsunami disaster mitigation in Padang City, Indonesia. Assessment consists of several steps, starting from the construction of tsunami hazard maps based on the most probable earthquake scenario in the future. Results are then analyzed to determine the impact on residential population along potential evacuation routes. Next, from the standpoint of hazards, we move to the analysis of human’s vulnerability during evacuation. The term “vulnerability” is associated with available evacuation time. Here, we conducted a static evacuation model using the GIS platform and a dynamic approach using multiagent paradigm. Results of evacuation modeling suggest that some residents may not have enough time to leave the tsunami inundation area before the first wave comes. We therefore propose using relatively high buildings as vertical evacuation sites. One of potential candidates that survived from a devastated earthquake with 7.6 Mw in 2009 is selected to be further analyzed its antiseismic deficiencies based on design ground motion obtained from micro-tremor analysis and synthesized recorded wave in Padang. As a result, even though the building underwent some damage, the frame structure was able to withstand the shaking and keep the building from collapsing. Keywords: tsunami hazard, tsunami casualty, evacuation model, ground motions
1. Introduction Motivated by recent earthquakes and tsunamis in Indonesia and in order to reduce future impact, especially in areas around the western part of the Sunda Trench, an international research program is being conducted to determine the most appropriate and applicable solutions for
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the mitigation of adverse effects. This paper focuses on the establishment of a social infrastructure based on engineering development. The study area is Padang, a coastal town on the west coast of Sumatra that overlooks the Indian Ocean. After the huge tsunami event in 2004 in the northern part of Sumatra Island, a series of large earthquakes occurred toward the southern part of the Sunda Trench. Padang City, located in the center of the west coast of Sumatra Island then became the focus of international concern due to its exposure to an existing seismic gap created by these recent earthquakes. This gap is potentially capable of generating an earthquake with a magnitude of Mw8.8 [1] to Mw8.9 [2]. Much effort has thus been conducted on increasing the community’s ability to respond to tsunamis [3, 4]. Efforts include both scientific analysis of tsunami hazards and risks [5, 6] and community-based tsunami preparedness activities [7, 8]. Based on experiences with previous earthquakes (2007, 2009, and 2010), however, it has been observed that community preparedness is still limited by the lack of infrastructure components such as informative tsunami hazard maps, official evacuation routes, evacuation signs, and vertical evacuation facilities inside predicted tsunami inundated areas. Although much scientific research, e.g., [5] previously conducted for tsunami hazard assessment in the city, outcomes do not seem to have been absorbed or reflected in the city’s tsunami disaster mitigation policy. The results of sophisticated technology in assessing hazard and vulnerability are not reflected when official evacuation maps [9] – which are the most important foundation in the development of tsunami evacuation strategy – are established and distributed to community. In order to provide comprehensive information as the basis of an integrated tsunami evacuation plan and by using practical tools with the expectation that they can be easily applied, maintained, and updated, we therefore performed a detailed analysis of tsunami hazards and an esJournal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
timation of the potential impact on residents along potential evacuation routes. Macro- and micro-scale observations of evacuation are then conducted to analyze potential problems during evacuation. Results of evacuation modeling suggest the necessity for vertical evacuation in the area. Damage to large-scale reinforced concrete buildings including that previously nominated as potential candidates to be used for tsunami evacuation sites during the devastating earthquake of 2009 [10], however, require that future proposed shelters be earthquake-resistant. For this reason, detailed surveys were carried out to assess the intensity of earthquake that causes damage to buildings. We conducted micro-tremor observation in addition to limited recorded wave data during the earthquake. Then, a severely damaged but still standing building was selected as a candidate for studying the earthquake-resistance of similar future vertical evacuation sites.
2. Tsunami Risk Analysis Padang City released an official tsunami evacuation map in September 2010 (Fig. 1) [9]. Such maps have become one of the most important milestones in tsunami preparation since 2005. Unfortunately, as previously mentioned, the evacuation map does not appear to contain the basic information necessary for evacuation planning. It is not clear how the boundaries of evacuation zones were determined, for example, because there is no information about the predicted tsunami inundation area or the estimated depth distribution in the evacuation zone. There is no information, furthermore, about shelter locations, evacuation routes, or tsunami arrival times at specific places inland. Fig. 1. Official tsunami evacuation map in Padang city.
2.1. Tsunami Hazard Assessment To improve this official map, we decided to perform a detailed tsunami hazard analysis and predict the impact to the community. First, potential earthquake sources were identified as scenarios for tsunami propagation and inundation in Padang. A historical source about tsunami that occurred in 1797 [11] was chosen as Case 1. Next, a prediction based on comprehensive geodetic, paleo-geodetic and micro-atoll research by Chlieh et al. [12] was selected as Case 2. Case 3 then considered studies from Natawidjaja et al. [2] which enhanced the model of Case 2 by accommodating the energy reduction caused by the 2007 earthquake and multiplied the slip 1.5 times. From a comparison of the 3 cases, Case 3 produces a wider wave period and higher initial sea surface height than the other two (Fig. 2, lower right). Case 3 was therefore used as the design scenario due to its initial sea surface height. The width of the fault near the coastline of Padang generates a subsidence of 1.7 m. This has an important influence on corrections of near-shore topographic and bathymetric data due to sea floor displacement (Fig. 2). Tsunami generation is calculated using the seismic deformation model by Okada Journal of Disaster Research Vol.7 No.1, 2012
(1985) [13]. Calculation is done by assuming that the entire fault area ruptures simultaneously, so there is no effect of rise time or rupture velocity. Since tsunami hazard analysis is conducted using a predicted earthquake, there is no verification data available to confirm modeling results. This raises the implication of uncertainty, especially when a tsunami has reached the coastal area, and has begun to inundate the city. One of the factors that influence inundation is the existence of the buildings. If building data is integrated into the digital elevation model, on the one hand, then its resistance in determining tsunami flow properties will work during simulation. If, on the other hand, there is no building information in topographic data, then flow characteristics will be influenced only by ground level. To accommodate the above considerations, we used the following assumptions regarding topographic data input. (i) No buildings survive the tsunami and the difference in the bottom friction coefficient without buildings is too small to affect tsunami flow. This is called the Constant Roughness Model (CRM). 49
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Fig. 2. Tsunami source model with nested grid system (left), contours of potential land subsidence in the city of Padang (upper right), and the cross section of the sea floor displacement model (lower right).
(ii) All buildings withstand the tsunami, and building information is integrated along with topographic data input. This model is thus called the Topographic Model (TM). Using the TM, the flow characteristic of a tsunami inundating the residential area can be well described. The speed difference between the tsunami when it flows in between settlement blocs is well illustrated, and the characteristic of tsunami flow depth and its changes when the tsunami flows through narrow streets and when it meets open spaces can also be described very well. As an implication of applying these two methods, the resulting tsunami hazard map will have two different limits of inundation – the first and second line – to depict the uncertainty in hazards posed by the tsunami (e.g., FEMA, 2008 [14]). The first line indicates the possible maximum inundation area from the CRM. This line is farther inland than the second line. The second line gives maximum inundation extents and dynamic flow properties if most of buildings in tsunami-affected areas survived (TM) during the run time of the numerical simulation. The predicted earthquake scenario shows that huge coseismic seafloor deformation extending up to the coastal area yields an “in-situ” tsunami generation source. The initial modeling assumption that seafloor displacement is similar to initial sea surface disturbances implies that the tsunami will be preceded by a negative wave. The first tsunami wave predicted reaches the coast within 20 minutes, and the highest wave arrives in 30 minutes and is 5 m in height (Fig. 3). Building on the results obtained by Muhari et al. [3] and using the same data sources, we took into account the sce50
Fig. 3. Time series of tsunami propagation observed at three virtual tide gauges.
nario of a tsunami arriving during high tide. Although the high water level is only about 0.8 m above the still water level, co-seismic land subsidence is likely to significantly change the area flooded by the tsunami. The results of the tsunami inundation model affected by the two methods are shown in Fig. 4 (left and center figures). The extent of the inundation areas generated by the CRM implies that at least 238,185 people are exposed during the day, and 232,886 are exposed at night (as detailed in the next section). With a total inundation area of 23.3 km2 , the CRM predicts an average flow depth of 3.2 m and a maximum of 7.9 m. It also produces an average flow velocity of 3.1 and a maximum of 16.1 m/second, respectively. The TM, however, produces a tsunamiaffected area of 12.9 km2 . This is much smaller than that produced by the CRM. As mentioned previously, Journal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
Fig. 4. Spatial distribution of maximum flow depth (left), velocity (center), and the Road Risk Map (right, explained in section 2.2). The yellow dotted line in the left and center figures indicate the maximum inundation line produced by the CRM.
the resistance of buildings works throughout the simulation time, and significantly reduces the kinetic energy that will prevent the tsunami from penetrating further inland. Not all buildings using this method are submerged by the tsunami, either, so the inundation extent is much smaller than with the CRM. An estimated 146,364 people are, nevertheless, threatened by the tsunami if it occurs during the day and 139,447 if at night. Compared to the official tsunami evacuation map in the city of Padang, the tsunami inundation extent from the TM is similar to the official one. Compared to the CRM, however, the official tsunami evacuation map is much smaller in its inundation extent. Here we can say that the area between these two inundation lines is the buffer area that residents should be taught about when evacuating once an evacuation order is issued. Utilizing the results of the inundation modeling, we are now proposing a tsunami hazard map with all information about tsunami inundation characteristics. The potential impact on residents during evacuation has not, however, been determined yet. It is necessary to identify which roads should be taken and which avoided in evacuation based on the tsunami inundation parameters of the respective roads. For this purpose, we use the results of the TM as the basis for the next step since it gives better detail on flow properties, especially in residential areas where evacuation activity will be started.
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2.2. Tsunami Risk on Residents We used the output from tsunami inundation parameters to estimate the hydrodynamic force acting on the human body to analyze the potential impact of the tsunami on residents. As a physical principal, once the hydrodynamic force exceeds the ability of a human being to remain standing, then the potential of becoming a tsunami casualty arises. Two important mechanisms causing people to lose their balance and fall are considered. The first is when the friction force on the soles of the feet is less than the hydrodynamic force acting on the body. The second is when the momentum on the back of the heel is less than hydrodynamic force [15]. If one of these two conditions is satisfied by the result of a tsunami inundation model at a specific point and at a specific time step, then a potential tsunami casualty is counted as an actual casualty. An improvement in the tsunami casualty model proposed by Koshimura et al. [16] is developed as detailed in Muhari et al. [17]. To obtain more realistic results, we utilize anthropometric data from Chuan et al. [18] to measure the size of specific body parts of Indonesian men and women. Verification of the human casualty model was conducted using experimental data from Takahashi et al. [15], the results of which are given in Fig. 5. The number of potential tsunami casualties is compared to the total time of tsunami inundation, then the Tsunami Casualty Index (TCI) is calculated. This term was introduced by Koshimura et al. [16], and is actually showing how many times the potential casualties occurred at a spe51
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Fig. 5. Tsunami casualty model verification using the experimental data of Takahashi et al. (2002). (A) is the slipping fall mechanism and (B) is the toppling fall mechanism.
cific point. The spatial distribution of the TCI is used to establish the Road Risk Map shown in Fig. 4 (right figure). From a spatial point of view, the road risk map illustrates the potential occurrence of tsunami casualties on a specific road if those who use the road cannot complete evacuation before the tsunami arrives. Important information given by the road risk map is that not all regions in tsunami-affected areas are dangerous to human safety. Areas that are relatively close to the maximum limit of tsunami inundation may still be flooded up to 0.5 m, but the low velocity of < 0.5 m/second does not cause people to lose their balance and be swept away by the tsunami. Average TCI values obtained from the model are 35% for adult men and 37% for adult women. This means that there is a 35–37% chance for adults losing their balance from the total time of tsunami inundation if they are still on roads with TCI values when the tsunami comes. Results reveal some critical points. First, roads with high TCI should be free from evacuation activities before a tsunami comes. These roads should be free from potential congestion during evacuation. Second, if the first option is not possible, then these areas should be prioritized to have vertical evacuation facilities to ensure the safety of evacuees. These points request further analysis of evacuation activities inside areas with high TCI values. We therefore performed detailed evacuation modeling as explained in the sections that follow.
3. Evacuation Model Due to the lack of vertical evacuation facilities inside the predicted tsunami inundation area, once an evacuation order is issued by the government, people are more likely to evacuate to avoid coastal areas as far as possible. This, of course, has the consequence that residents will strive as soon as possible to reach high ground or to get away from coastal areas. The option of using vehicles consequently becomes inevitable and the possibility of congestion will be even greater than that which would 52
Fig. 6. Traffic congestion during evacuations after the 2009 earthquake.
keep people from getting to a safe place before a tsunami cames. Fig. 6 shows a traffic jam during evacuation in the 2009 earthquake. The main objective of the evacuation model described in this section is to identify the time needed by evacuees to leave the tsunami inundation zone in general and to determine potential congestion points in particular, especially inside areas with high TCI values. Two kind of evacuation model were used. The first is a static evacuation model conducted under the GIS platform and the second is a dynamic evacuation model developed under the multiagent-based evacuation modeling. The concept of the first model is to determine the reduction in walking speed due to the influence of external factors related to the environment surrounding the evacuees. This means that we analyzed evacuation time in a spatial framework. In contrast, the second model focuses analysis on the internal factors affecting evacuees especially the aspect of different evacuation starting time, and models the evacuation behavior of each agent representing evacuees in the study area. We use census-based population data [19] on each village in study area to prepare detailed population distributions for day and night. Approaches were available previously to model the population distribution in Padang City based on building classification and size to calculate the number of residents inside [20], and based on livelihood and land-use to determine the percentage of the population located in a specific land use area at day and night [21]. In this study, we used a more practical way through the following simple equations to disaggregate population data from village based into building based population: Pi Ph
= (Di Ai )/S pi . . . . . . . . . . . (1) = [Dh (H/N)] + G . . . . . . . . . (2)
Eq. (1) is used for nonresidential buildings. In this equation, Pi is the number of inhabitants in each type of building, Di is the percentage of building area used by people day and night, Ai is the building area derived from Journal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
Fig. 7. Population distribution from basic census data (left), result of population distribution model in a daytime scenario (center), and in a nighttime scenario (right).
GIS analysis, and S pi is the area needed by people in a specific type of building. Eq. (2) is used for residential buildings and typical house-store buildings. Here, Ph is the number of people in each house obtained by averaging the number of persons versus the number of house and house-store buildings based on the number household H in each desa (village), Dh is the percentage of family members at day and night, N is the total number of residents within a village, and G is the average number of patrons at house-store building during the day. The population distribution model is compiled from census data based on residential addresses, which typically describes the population at night. If we thus assumed that the accumulation of people in buildings other than houses, i.e., offices, malls, etc., only comes from inhabitants within the study area, then the total population during the day and night should be similar to the total population of the all villages. Fig. 7 shows the population of each village, totaling 293,252 people. The results of our model give a total population of 287,738 for a daytime scenario and 296,514 for a nighttime scenario. The difference in numbers is due to the identification of buildings that may not be classified correctly. This reduces the number of buildings, which are areas of high population density during the day compared to the number of houses. For the night scenario, the possible misidentification of buildings that are considered to be houses causes model results to be slightly larger than population data. The error in this model is, however, less than 1.8% for the daytime scenario and 1.1% for the nighttime scenario. Journal of Disaster Research Vol.7 No.1, 2012
3.1. Static Evacuation Model In this section, the influence of land use, population density, critical facilities, the demographic index, and topographic slopes in reducing normal walking speed of residents is determined using a GIS-based evacuation model [22]. These items are then given specific weighting factors to obtain the spatial distribution of travel time within the tsunami inundation zone. In other words, there are no time-dependent variables or behavioral aspects of evacuee considered in this method. The results of the population distribution model are compiled with land cover data, the demographics index, critical facilities, and slope data to be weighted to describe the influence of the above factors in reducing the walking speed. Compiled data is then overlaid with tsunami inundation lines obtained from the above numerical model to identify exit points from the tsunami inundation zone. Next, by using ArcGIS software cost distance tools, we established an evacuation time map, which describes the spatial distribution of time to leave the tsunami inundation zone. The flow of this model is shown in Fig. 8. We modeled the evacuation time map for a daytime scenario. The results shown in Fig. 9, suggest that the option of horizontal evacuation is not able to guarantee that all people can leave the tsunami-affected area before a tsunami arrives. If the assumed threshold of tsunami arrival time is 20 minutes, only 100,847 people of a total 146,364 people exposed by tsunami hazard are estimated to be able to get out of the tsunami inundation zone. Most of those not having enough time to do so lived near 53
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Fig. 10. Distribution of start time evacuation curves.
Fig. 8. Flow process of static evacuation model.
Fig. 9. Evacuation time map for city of Padang, black lines indicate areas that have the fastest evacuation time to a specific exit point given by black dots. The inset shows the area for further analysis in a micro-scale evacuation model.
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beaches. When summed up with the number of residents in areas with an evacuation time of 30 minutes, however, as many as 136,657 people could leave through existing egresses. Note that on the northern border of the study area, we assume that the only path out of the tsunami inundation zone is toward the west (to the right of the model boundary). In this area, then, it is consequently not possible to choose a path toward the north parallel to the coastline. In the area indicated by the inset in Fig. 9, 73,191 of the total population of 91,796 can manage to leave of the tsunami inundation zone within 20 minutes. When summed up with the number of people residing in an area having a 30-minute evacuation time, 91,456 people – which is almost 100% of the total population – can escape from a tsunami through existing ways out. Results thus look ideal but do not reflect potential problems that may occur during evacuation. In the next step, we tried to see how a different evacuation start time at the individual level influences these cases in order to determine potential problems during evacuation.
3.2. Dynamic Evacuation Model Noting the influence of external factors in quantifying the response capacity of the community, Post et al. [22] described the four most relevant factors – namely decision-time warning, dissemination time, reaction time, and evacuation time. Reality during evacuation, however, is usually not as smooth as expected even though we try to minimize the time for each phase. Depending on the culture, infrastructure, and knowledge of tsunami hazards that vary with each region, the influence of each factor on the four elements in [22] may be different. In the reaction time phase, Riad et al. [23] found that the decision to evacuate is related to three things – namely, risk perception, social factors, and access to information. In Japan, for instance, where a tsunami-early warning system has been developed with very sophisticated technology, early warning and evacuation orders from the government are a major factor in determining the success or failure of the evacuation process. Unlike the case of Simelue Island, Indonesia, during the 2004 Indian Ocean tsunami (e.g., McAdoo et al. [24]), however, the absence of a tsunami early warning system does not prevent efJournal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
Table 1. Parameters for the distribution of start time evacuation.
fective evacuation or a reduced number of casualties. Local lore about tsunamis has become one basis of decisionmaking in communities: experience has taught residents to respond to earthquakes by running to higher ground in order to evade tsunamis. In this context, experience plays a great role in determining the decision to evacuate. All of these factors lead to evacuation start time differences among persons based on one of the most powerful influences among the three factors mentioned above. In the following sub-sections, we examine the effect of different start times on evacuations on the individual level to determine patterns and implications on the regional scale, such as potential bottle necks and route selection. The results of this step are used to look into potential problems that may arise before and during evacuation. To this end, a model was developed that accommodates different evacuation start times in an agent-based evacuation model [25]. 3.2.1. Modeling Overview The model for tsunami evacuation was developed in Netlogo, a multiagent programming language and modeling environment for simulating complex phenomena [26]. Hundreds or thousands of “agents” can operate concurrently in order to explore the connection between the micro-level behavior of individuals and the macro-level patterns that emerge from their interactions. To execute a simulation, the modeler can decide, from the input parameters and scenario alternatives, the individual or group behavior to be explored. Although an arbitrary number of agents can be used, the desired situation, such as that modeled here, is to import agent characteristics and population distribution from a GIS dataset. We used the population distribution of Padang for a day scenario as derived in the previous section. Other GIS datasets such as topographic and bathymetric elevations are imported from raster formats, at the same time while street dataset is converted from a GIS vector dataset into the currently used environment grid size of 5 m×5 m. The structure of agent behavior is simple to follow. At the beginning of simulation (t = 0), an agent is located in the environment based on population distribution data available. At this stage, one single type of agent – a standard human – is considered. Behavior and preferences are assigned and revealed during simulation. The first important parameter is the start time of evacuation for each agent. The model assumes that all agents involved in the scenario have the intention to evacuate at some point in time. The whole population is therefore given an evacuation start time distribution. This is the time of the decision to evacuate which may be fast or slow in relation to the arrival time of the tsunami. The point in time given to each Journal of Disaster Research Vol.7 No.1, 2012
agent is based on a statistical distribution of preparation times, which is the time spent by an evacuee before deciding to evacuate. From the analysis of several questionnaire data items on the start time of evacuation in past events and future expectations of respondents, the Rayleigh distribution gives a better prediction for preparation time, which is the time needed by the evacuee to make a decision to evacuate in evacuation procedures [27, 28]: F(t) = 1 − exp(−t 2 /2σ 2 )
. . . . . . . . (3)
Eq. (3) shows the cumulative distribution function of Rayleigh distribution, where σ is the mode of the preparation time of the sample. Basic information used in determining the distribution function of the start time of evacuation is based on a questionnaire conducted in Padang city [29] two months after the September 2009 M7.6 earthquake. According to the questionnaire, most respondents evacuated low-lying coastal areas in a relatively short time – by 15 minutes after the tremor, 83% of them had left. We used the value of 15 minutes as the mode for obtaining the best fit of distribution with questionnaire data (Mean = 20 min) as given in Table 1. The obtained distribution in Fig. 10 is used as final input for start time evacuation for agents. Here, we state the limitation of the model in relation to the confidence level of the statistical assumption of modeling a whole population with the same distribution as the sample. Due to a lack of information at this stage, distribution related to the 2009 questionnaire result is used, although it is statistically risky to assume the same distribution for the 150,000 agents modeled here. A standard procedure should be to determine sample size with a decided level of confidence, e.g., 95%, z = 1.96, and maximum error, e.g., +/−5%, e = 0.05, in order to control the statistical reliability of the outcome distribution. Another approach is the stochastic simulation of several distributions for preparation times. Until that step is fulfilled, preliminary evaluations will be made using the shape of distribution above. After all agents in the environment know their start time evacuation, the decision for evacuation is conducted when the time comes and movement is made to the street. Once in the street, the agent decides the shelter location of preference. The preference selected using this model is the nearest shelter or exit point. At this stage, the “classical” path-finding problem is solved by the widely used A* (A star) search algorithm [30] modified in grid space and Netlogo language. The A* search algorithm uses a best-first search and finds the least-cost path from a given initial grid (patch in Netlogo) to one out of many possible goal patches. The distance-plus-cost heuristic used here 55
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Fig. 11. Domain area for dynamic evacuation model (The inset in area 3 indicates the location of the BPKP building in section 4.2).
is denoted as f (x) and is the sum of two functions: (a) g(x), the path-cost function, which is the cost from the starting patch to the current patch. (b) h(x), the heuristic estimate of the distance to the goal. There are several heuristics or several ways of estimating the distance to the goal. These heuristics represent the spatial bias of individual’s estimated locations in relation to shelter. Agents will therefore not necessarily choose the shortest path, but will choose any path that leads from their location to the selected shelter. After a route and shelter are decided, evacuation is finally conducted at a constant average speed of ∼1.67 m/s. For the dynamic movement of agents, we used a simplification of Predictive Collision Avoidance proposed by Karamouzas et al. [31], using the corridor test of Helbing (1995) [32]. We count the maximum number of agents that can share a space in dynamic motion. This parameter is later used as the maximum number of agents allowed at the same time in a specific area (patch). Other agents with the intention of moving to this area must wait until space becomes available. Agents will follow their individual selected path, emergent behavior as bottlenecks, and shelter preferences, and route demands can be observed and reported in simulation. At the same time as agent are evacuating, tsunami output from a tsunami inundation model as given in the previous section are imported into the evacuation environment taking inundation depth and flow velocity into consideration for casualty estimation. We use the same criteria for the term “casualty” as described before. Once one of the criteria is met, however, then an agent is considered to be a victim of the tsunami.
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Fig. 12. Comparison between number of start time evacuations and evacuation time toward selected exit point to percentage of evacuee.
3.2.2. Modeling Results We modeled a total of 104,352 agents in a total area of 3.4 km × 4.5 km including the non-inundated area shown in the inset in Fig. 11. This is a large scenario resulting in a time- and memory-consuming run. We therefore split the area into 3 sub-areas and perform simulation independently. Since the area is uniformly distributed vertically, shelters and exits are located to the right of the calculated area and it is assumed that the exchange rate of evacuees between subareas is set limited, so the priority direction of agents is perpendicular to the shoreline. All roads leading out of the domain mostly at the right side boundary are considered to be exit points. The model is run for 45 minutes in simulation so that the highest tsunami wave height can be accommodated. Figure 12 shows the start time of evacuation distribution and the completeness of evacuation time. From the evacuation start-time distribution, it appears that 5 to 10% of the population requires longer than 45 minutes to make an evacuation decision. We widened the distance between the two curves indicating that more time is required by evacuees to leave the tsunami inundation zone. This inJournal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
7,132 agents who planned to leave the tsunami inundation zone through this point were capable of reaching this point. In sub-area 2, there are two places where congestion is predicted to occur during evacuation. These are the largest shopping center in Padang City, where one of them is a shopping mall and the other is a traditional market center. As shown by the results of the population distribution model (Fig. 7, center), both of these places have a very high population density in daytime – 1,000-2,000 people/hectare. In sub-area 3, the greatest demand was at exit point 10, but only 329 from a total of 11,258 managed to reach this point within 45 minutes. Another issue that emerged in this area is the tendency of agents to choose a path along the river bank due to the consideration of shorter distances. In the actual course, however, this is very dangerous because results from the tsunami inundation model show that this area is at risk for being flooded by the tsunami overflowing from the river embankment.
4. Necessity of Vertical Evacuation Building
Fig. 13. Number of demands and evacuated person at each exit point.
directly indicates that more and more problems occurred during evacuation. To briefly describe the evacuation routes that are most likely to be used by evacuees, we plotted a comparison between the number of people who decided to evacuate to a specific exit point in each area – here and afterward considered “demand” – and the number of those who successfully reached the target point in Fig. 13. In sub-area 1, from a total of 24,803 people who made the decision to evacuate to a specific exit point, only 5,005 people, or 20.2%, reached the destination point. In sub-area 2, only 13,691, or 44.5%, of a total of 30,765 people who decided to evacuate managed to leave tsunami-affected areas. In sub-area 3, from a total of 42,190 people who decided to evacuate, only 18,180, or 43.1%, are estimated to be able to reach their planned exit points. As shown in Fig. 13 area 1, many agents decided to pick out exit points 1 and 2 as the evacuation target point, but the high demand for these points led to congestion when agents met at intersections running in opposite directions. From a total of 10,182 agents who planned to evacuate through exit point 1, only 432 could pass before 45 minutes. Also at exit point 2, only 5 out of a total of Journal of Disaster Research Vol.7 No.1, 2012
It is observed from evacuation modeling results that in a worst case scenario, only 37.7% of the population is able to leave the tsunami affected area even if the evacuation time is extended up to 45 minutes. This occurrence happened in some previous experience of tsunami evacuation in the city. The important point is surely that the city needs more evacuation sites within the area that is predicted to be inundated by a tsunami. Due to the dense population and the lack of available space to build new facilities for evacuation, however, one option is to use existing high buildings in the area. The first requirement for these structures to be used for evacuation is earthquake resistance.
4.1. Design Ground Motion Based on 2009 Earthquake Experience On September 30, 2009, an earthquake of Mw7.6 struck the west coast of Sumatra, affecting Padang and Pariaman cities, causing significant damage to houses and buildings, as well as killing more than 1,000 people. Damage features differed site-dependently. One of the marked features of this disaster was damage to largescale reinforced concrete buildings in Padang, the capital city of West Sumatra. A survey and detailed analysis in a damaged building and the shaking intensity of the ground during the earthquake was conducted in order to determine the antiseismic deficiencies of this building. Since the shaking during this earthquake was not recorded by instruments in the downtown area of Padang, however, an estimation of its intensity and its predominant frequency are analyzed through two approaches, namely, micro-tremor observation and wave synthesis using a recorded wave on a rock site near Padang. 57
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Fig. 14. H/V spectra for micro-tremors in Padang City.
Fig. 15. Conceptual diagram of synthesizing process.
Micro-tremors at typical sites in the downtown area of Padang were observed as one way to estimate the seismic response feature of ground, and the observed data was analyzed by the H/V spectrum method (Fig. 14). The rather long-period components of one to two seconds are clearly predominant, and they seem to lengthen from south to north. There is a hilly area on the left bank of the river mouth of Mata Air Timur (see lower left in the of Fig. 14 inset map), so the surface soil layer is assumed to be shallow in the southern area and to become thick in the north. This assumption is supported by the variation in the predominant period of micro-tremors from south to north. We use data from The Indonesian Meteorological Agency (BMKG), which recorded shaking using a strongmotion seismograph placed on a rocky site at Andalas University (a local university in West Sumatra Province located on about 11 km east of the downtown area of Padang), and array observation using three seismographs
58
located at the Andalas University site, a stiff soil site, and a soft soil site of the downtown area was being operated by EWBJ (Engineers without Borders, Japan) and Andalas University to synthesize a provisional shaking in the downtown area during the September 30, 2009 earthquake, which conceptual diagram is shown in Fig. 15. We first extracted a Fourier transform function to average 4 earthquake records recorded from April to December 2009, which were 0.25-1.25 kine (cm/sec) at maximum. We then composed an assumed soil column model fitting the transfer function. Depth and Vs were determined by trial and error. The nonlinear (equivalent linearized) response of the soil column model using the BMKG record as the incident wave is calculated. An improved SHAKE code [33] with frequency-dependent damping was used. The synthesized wave and the response spectrum are shown in Fig. 16. Due to the effect of the soft soil layer, the short-period component of the incident wave is cut off and the peak acceleration is
Journal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
Fig. 16. Incident wave (left), synthesized wave (center), and response spectrum (right).
Fig. 17. BPKP building (upper left) and typical column damage based on damage degree in Table 2, damage degree III (upper right), IV (lower left), and V (lower right).
decreased. The long-period component, however, is amplified and the relative response velocity at 1.8 seconds reaches 150 kine. JMA instrumental seismic intensity is 5.3 and MMI is about VIII. 4.1.1. Detail Survey and Numerical Analysis on a Typically Damaged Building After obtaining the synthesized ground motion in down town area of Padang, we conducted an analysis of damage to large buildings caused by this earthquake. Depending on the result of this assessment, an analyzed building may be proposed as a candidate for tsunami evacuation site in the future. The building selected was the BPKP (Financial and Development Supervisory Board) building, located at the center of the downtown area around 1.5 km from the shoreline and at an intersection near the river, close to one of the potential area for congestion during evacuation (Fig. 11, inset in area 3). Built in 2003, it was severely damaged but remained mostly standing (Fig. 17). During the on-site survey, cover materials near the top and bottom of all columns were removed and the degree Journal of Disaster Research Vol.7 No.1, 2012
Table 2. Damage degree frequency by floor.
of damage to each one was evaluated, as shown in Table 2. All of the major building components, namely, columns, floor heights, beams, plate thickness, and reinforcing bars, were measured. Main bars were from φ 19 × 16 to φ 17 × 12 and stirrups were φ 10 spaced at 120 mm to 150 mm. The concrete strength of representative portions was measured with a Schmidt Hammer, and steel bar strength was measured with a Vickers Hardness 59
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Fig. 18. Frame model and analyzed natural vibration.
Tester. The micro-tremor of the building was also measured. Using these measured dimensions, a lumped mass frame model was developed, with weights of inner and perimeter walls of the building included in the lumped floor-plate mass. The stiffness of columns and beams was assumed to be 100% of the original elastic-range value. The frame model was analyzed using a versatile software system for structural analyses, and the natural periods of free vibration and the response to a design earthquake load were evaluated. Fig. 18 shows analyzed modes and natural periods. Periods in the X direction and in torsion coincided with those of the micro-tremor. The period in the Y direction, however, was different. The model did not take into account remaining stiffness of brick walls and decreasing stiffness of damaged columns. These two factors must have compensated in the X direction and in torsion. The damage was considerably smaller in the Y direction than in the X direction, thus suggesting that wall stiffness in the Y direction may not have decreased much. Using concrete and steel bar properties obtained from the survey, we calculate the axial force and moment capacity diagram of columns at all floor levels. To compare these capacities with actual stress levels in columns, equivalent static seismic loading was applied based on SNI-03-1726-2002 [34] (a recent seismic code used in Indonesia). Dead weight was assumed to include weights of structural members (columns, beams, and plates) and perimeter walls, and live load (20 N/mm2 for offices). The equivalent static seismic coefficient was also assumed based on the [34]. Nominal static equivalent base shear force V thus is: V = (C1 I/R)Wt
. . . . . . . . . . . . (4)
where C1 is obtained using first natural period T1 , and Wt is total building weight, including an appropriate live load. In the BPKP building case, T1 is 1.11 seconds. Therefore, based on the figure and soft soil (Tanah Lunak) condition, C1 is 0.9/T1 = 0.9/1.11 = 0.811. This is an importance factor, taken as 1.0 for office buildings. R is a seismic reduction factor. For a normal moment resisting frame, R is taken as 3.5. V therefore becomes 0.232. Weight is quantified as Wt = 39, 005 kN follow-
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Table 3. Demand moment vs. Capacity moment.
Fig. 19. Comparison of C1 curve to the synthetic response spectrum.
ing the assumption above. The base shear force is thus V = 0.232 × 39005 = 9, 036 kN. Base shear V must be distributed along the height of the structure, i.e.: Fi = (Wi Zi )V /
n
∑ WiZi
. . . . . . . . (5)
i=1
where Fi is the load acting on the mass center at floor −i, Wi is the weight of floor −i, Zi is the height of floor −i, and n is the total number of stories. Based on three-dimensional frame analysis, the axial force and the demand moment at a typical column of the 1st and 3rd floor are shown together with the moment capacity in Table 3. Demand moment exceeds capacity, so the column could collapse if actual seismic force reached the level of the design seismic load denoted in [34]. Figure 19 compares the C1 curve to the synthesized wave response spectrum. The C1 curve is also shown to the capacity moment level of the third floor column. It is clear that seismic force acting on the BPKP building Journal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
during the September 30, 2009 earthquake far exceeded its capacity and reached the level of the design seismic load denoted in [34]. According to the design document of the BPKP building, it was designed based on the previous design code, before [34]. Design calculation was based on the seismic coefficient method and the design seismic coefficient was 0.07. In spite of the very small design load, the advantage of a moment frame structure may have contributed to avoiding collapse. As a finding and a recommendation for the retrofit of this building, if the reinforcing bar arrangement, especially hoops, had been adequate, columns may not have been so severely damaged. This method and criteria of the evaluation should be conducted for other possible candidates for tsunami evacuation buildings to ensure that they will be able to withstand at least similar ground shaking in the future.
5. Concluding Remarks A series of study to provide comprehensive tsunami disaster mitigation has been conducted in Padang city, Indonesia. The important findings revealed by this research are as follows: 1. In preparing tsunami mitigation plans, hazard information that must be taken into consideration is the worst of several available scenarios. Within this framework, our results show that there are two sources of uncertainty that determine the difference in the results of numerical tsunami simulation. First are the seismic parameters used in the tsunami generation model, and the second is the assumptions used in the resistance law when a tsunami starts to penetrate inland. Using these considerations in development, a hazard map may yield the implication that the maximum tsunami inundation extent exceeds the evacuation zone indicated by the existing official map. Lessons from the 2011 tsunami in Japan where, in most areas, the tsunami inundation was significantly farther than predicted limits implies that the results from the present study can be taken into consideration in reviewing the existing official evacuation map and to enrich content with valuable information. 2. We introduce a new type of tsunami risk map by integrating the results of a tsunami casualty model into a spatial point of view. Here, the road risk map brought us some critical implications, i.e., that the evacuation route consists of roads with high TCI that should be free from evacuation activities before a tsunami comes. These roads should therefore be clear from congestion during the evacuation. If previously mentioned conditions are not possible, then these areas should be prioritized to have vertical evacuation facilities to ensure the safety of evacuees. 3. Approaches used in the evacuation model yield an important conclusion: under normal conditions by Journal of Disaster Research Vol.7 No.1, 2012
considering surrounding environmental conditions and static population density, safe areas may be able to be reached in a relatively short time. During an emergency, however, starting time evacuation and selected evacuation routes can give a different outcome. Here, we introduce a new method to estimate starting time distribution based on a questionnaire in study area. Some limitations related to the number of respondent arise, but by increasing the number of respondents, better representative results can be obtained. 4. Results from a dynamic evacuation model demonstrate that most residents in the city of Padang may not have enough time to evacuate from the predicted tsunami inundation area. Some of them may also be stuck in traffic jams on streets with high TCI values. In this case, we demonstrated the urgent need for more options for vertical evacuation inside the tsunami inundation area. One of the potential solutions for mitigation is to use existing high-rise buildings for evacuation. By analyzing the M 7.6 earthquake in 2009, however, micro-tremor H/V spectra showed that downtown area of Padang has a predominated period of 1.0 -2.0 seconds, while the synthesized wave produces a predominant period of 0.5-2.0 seconds. This means buildings located in downtown of Padang with predominant period of 1.0-2.0 second (i.e., multi-storey building) would be most affected. Looking at the response spectrum that almost reached the design spectrum level denoted by Indonesian building code (SNI-03-1726-2002), our results of BPKP building conclude that if the designed code would have been actually implemented, damage or collapse of large-scale concrete building would have been prevented. Acknowledgements We express our deep appreciation to JST-JICA project (Multidisciplinary hazard reduction from earthquakes and volcanoes in Indonesia) group 3, and the Ministry of Education, Culture, Sports, Science and Technology (MEXT) Japan for financial support through the study No.(22241042). The authors thank for the suggestions and very constructive comments from the reviewers to improve the quality of the manuscript.
References: [1] K. Sieh, D. H. Natawidjaja, A. J. Meltzner, C. C. Shen, H. Cheng, K. S. Li, B. W. Suwargadi, J. Galetzka, B. Phillibosian, and R. L. Edwards, “Earthquake Supercycle Inferred from Sea-Level Change Recorded in the Coral of West Sumatra,” Science 322, pp. 16741677, 2008. [2] D. H. Natawidjaja, K. Sieh, W. Kongko, A. Muhari, G. Prasetya, I. Meilano, H. Latief, and M. Chlieh, “Scenario for future megathrust tsunami event in the Sumatran subduction zone,” Asia and Oceania Geosciences Society (AOGS) 6th Annual meeting, Singapore, 2009. [3] A. Muhari, F. Imamura, D. H. Natawidjaja, J. Post, H. Latief, and F. A. Ismail, “Tsunami mitigation efforts with pTA in West Sumatra Province, Indonesia,” Journal of Earthquake and Tsunami, Vol.4, No.4, pp. 341-368, 2010. [4] H. Spahn, M. Hoppe, H. D. Vidiarina, and B. Usdianto, “Experience from three years development for tsunami early warning in Indone-
61
Imamura, F. et al.
[5]
[6]
[7] [8] [9]
[10]
[11]
[12]
[13] [14] [15]
[16] [17]
[18] [19] [20]
[21]
[22]
[23]
[24]
[25]
62
sia: challenges, lessons and the way ahead,” Natural Hazard and Earth System Science (10), pp. 1411-1429, 2010. H. Taubenbock, G. Goseberg, N. Setiadi, G. Lammel, F. Moder, M. Oczipka, H. Klupfel, R. Wahl, T. Schlurmann, G. Strunz, J. Birkmann, K. Nagel, F. Siegert, F. Lehman, S. Dech, K. Gress, and R. Klein, “Last-mile preparation for a potential disaster – Interdisciplinary approach toward tsunami early warning and an evacuation information system for the coastal city of Padang, Indonesia,” Nat. Hazard and Earth Sys. Sci. 9, pp. 1509-1528, 2009. G. Gayer, S. Lechska, I. Nohren, O. Larsen, H. Gunther, “Tsunami inundation modeling based on detail roughness maps on densely populated area,” Nat. Hazard and Earth Sys. Sci. 10, pp. 1679-1687, 2010. F. Imamura, “Dissemination of information and evacuation procedures in the 2004-2007 tsunamis, including the 2004 Indian Ocean,” Journal of Earthquake and Tsunami, Vol.3, No.2, pp. 59-65, 2009. Komunitas Siaga Tsunami – Tsunami Alert Community (KOGAMI), 2011, http://www.kogami.or.id/, last access August, 2011. Padang City Government and Mercy Corps, Peta Evakuasi Tsunami Kota Padang, 2010, http://www.gitews.org/tsunamikit/en/id tsunami evacuation map padang.html, last accessed Dec. 2011. The Headquarters for Earthquake research Promotion News, the September 2009 Padang, West Sumatra earthquake: International collaborative research on Earthquake in Indonesia, Vol.3, No.4, pp. 4-5, 2010. D. H. Natawidjaja, K. Sieh, M. Chlieh, J. Galetzka, B. W. Suwargadi, H. Cheng, R. L. Edwards, J. P. Avouac, and S. N. Ward, “Source parameters of the great Sumatran megathrust earthquakes of 1797 and 1833 inferred from coral microatolls,” Journal of Geophys Res. Solid Earth, doi: 10.1029/2005 JB004025, 2006. M. Chlieh, J. P. Avoac, K. Sieh, D. H. Natawidjaja, and J. Galetzka, “Heterogeneous coupling of the Sumatran megathrust constrain by geodetic and paleogeodetic measurement,” J. Geophyisical Research DOI 10, 1029/2007JB004981, 2008. Y. Okada, “Surface deformation due to shear and tensile faults in a half-space, Bulletin of Seismological Society of America, Vol.75, No.4, pp. 1135-1154, 1985. Federal Emergency Management Agency (FEMA), Guideline for design for structures for vertical evacuation from tsunamis, FEMA 646, pp. 36-37, 2008. S. Takahashi, K. Endoh, and Z. Muro, “Experimental study on people’s safety against overtopping waves on breakwater, Report of Port and Harbor Research Institute., Vol.31, No.4, pp. 3-29, 1992 (In Japanese). S. Koshimura, T. Katada, H. Mojfield, and Y. Kawata, “A method for estimating casualties due to tsunami inundation flow,” Nat. Hazard, Vol.39, pp. 265-274, 2006. A. Muhari, F. Imamura, S. Koshimura, and J. Post, “Examination of three practical run-up model for assessing tsunami impact on highly populated area,” Nat. Hazards Earth Syst. Sci., 11, pp. 3107-3123, 2011. T. K. Chuan, et al., “Anthropometry of the Singaporean and Indonesian populations,” International Journal of Industrial Ergonomics, 40, pp. 757-766, doi:10.1016/j.ergon.2010.05.001, 2010. BPS , Indonesian Statistical Bureau, ‘Village Potential Data’, 2006 (CD-ROM). H. Taubenbock, J. Post, A. Roth, G. Strunz, R. Kiefl, S. Dech, and F. Ismail, “Multi-scale assessment of population distribution utilizing remotely sensed data, the case study Padang, West Sumatra, Indonesia,” International Conference on Tsunami Warning (ICTW) Bali, Indonesia, November 12-14, 2008. M. R. Khomarudin, G. Strunz, R. Ludwig, K. Zosseder, J. Post, W. Kongko, and W. S. Pranowo, “Hazard analysis and estimation of people exposure as contribution to tsunami risk assessment in the west coast of Sumatra, the south coast of Java and Bali,” Z. Geomorphol., 54, Suppl. 3, pp. 337-356, 2010. J. Post, S. Wegscheider, M. Muck, R. Kiefl, T. Steinmetz, and G. Strunz, “Assessment of human immediate response capability related to tsunami threat in Indonesia at a sub-national scale,” Natural Hazard and Earth System Science, Vol.9, pp. 1075-1086, 2009. J. K. Riad, F. H. Norris, and R. B. Ruback, “Predicting evacuation in two major disaster: Risk perception, social influence, and access to resources,” Journal of Applied Phsycology, Vol.29, No.5, pp. 918934, 1999. B. G. McAdoo, L. Dengler, G. Prasetya, and V. Titov, “Smong: How an oral history saved thousands of Indonesia’s Simeulue Island during the December 2004 and March 2005 tsunamis,” Earthquake Spectra, Vol.22, No.S3, pp. 661-669, 2006. E. Mas, F. Imamura, and S. Koshimura, “Tsunami Risk Perception Framework for the Start Time Evacuation Modeling,” XXV IUGG General Assembly. International Association of Seismology and Physics of the Earth’s Interior, Melbourne – Australia, 28 June– 7 July, 2011.
[26] U. Wilensky, Modeling Nature’s Emergent Patterns with Multiagent Languages, EuroLogo, 2001. [27] F. Southworth, Regional Evacuation Modeling: A State of the Art Reviewing. Technical report, OAK Ridge National Laboratory, 1991 [28] S. Tweedie, J. Rowland, S. Walsh, R. Rhoten, and P. A. Hagle, “Methodology for estimating Emergency Evacuation Times,” The Social Science Journal, Vol.23, No.2, pp. 189-204, 1986. [29] M. Hoppe and H. S. Mahadiko, “30 minutes in Padang – lessons for tsunami early warning and preparedness from the earthquake on 30 September 2009. GTZ-GITEWS project publication,” www.gitews.org/tsunami-kit [30] P. Hart, N. Nilsson, and B. A. Raphael, “Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions of Systems Science and Cybernetics, Vol.SSC-4, No.2, pp. 100-107, 1968. [31] I. Karamouas, P. Heil, P. van Beek, and M. Overmars, “A Predictive Collision Avoidance for Pedestrian Simulation,” MIG 2009, LNCS 5884, pp. 41-52, 2009. [32] D. Helbing and P. Molnar, “Social force for pedestrian dynamics,” Physical review, E51, No.5, pp. 4283-4286, 1995. [33] P. B. Schnabel, J. Lysmer, and H. B. Seed, SHAKE: a computer program for earthquake response analysis of horizontally layered sites, UCB/EERC-72/12, Earthquake Engineering Research Center, University of California, Berkeley, 1972-12, p. 92 (480/S36/1972), 1972. [34] Departemen Permukiman Dan Prasarana Wilayah (2002) Standar Perencanaan Ketahanan Gempa Untuk Struktur Bangunan Gedung, SNI – 03- 1726 – 2002, Badan Penelitian Dan Pengembangan Permukiman Dan Prasarana Wilayah, Pusat Penelitian Dan Pengembangan Teknologi Permukiman, April 2002
Name: Fumihiko Imamura
Affiliation: Professor of Tsunami Engineering, Disaster Control Research Center (DCRC), Graduate School of Engineering, Tohoku University
Address: Aoba 6-6-11, Sendai 980-8579, Japan
Brief Career: 1992 Assoc. Prof., DCRC, Tohoku Univ. 1993-1995 Assoc. Prof. of Asian Institute of Technology 1997-1998 Affiliate Assoc. Prof. DPRI, Kyoto University 2000 Prof., DCRC, Tohoku University
Selected Publications:
• “Field investigation on the 2004 Indian ocean tsunami in the southwestern coast of Sri Lanka,” Proc. of special Asian tsunamis session at APAC (Asian and Pacific Coasts) 2005, pp. 93-106, 2005. • “Effects of coastal forest on tsunami hazard mitigation, Tsunami – Case studies and recent developments,” Springer, pp. 279-292, 2005. • “A huge sand dome formed by the 1854 earthquake tsunami in Suruga bay, central Japan,” ISET Journal of earthquake technology, No.462, Vol.42, No.4, pp. 147-158, 2005.
Academic Societies & Scientific Organizations: • Japan Society of Civil Engineering (JSCE) • American Geophysical Union (AGU) • Japan Society of Natural Disaster Science (JSNDS)
Journal of Disaster Research Vol.7 No.1, 2012
Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia
Name:
Name:
Abdul Muhari
Mulyo Harris Pradono
Affiliation:
Affiliation:
Doctoral Student of Tsunami Engineering, Disaster Control Research Center (DCRC), Graduate school of Engineering, Tohoku University
Specialist Engineer, Division of Disaster Mitigation Technology, Agency for the Assessment and Application of Technology (BPPT)
Address:
Address:
Aoba 6-6-11, Sendai 980-8579, Japan
Gedung 2, Lt. 18, Jl. M.H. Thamrin 8, Jakarta 10340, Indonesia
Brief Career:
Brief Career:
2005 Ministry of Marine Affairs and Fisheries, Republic of Indonesia 2008-2009 Visiting Researcher, German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Germany 2009 Doctoral Student, DCRC, Tohoku Univ.
2010-2011 Chief Engineer, Program of Disaster Risk Reduction Technology on Floods and Technological Failure 2009-2011 Group Leader, Project of Multidisciplinary Hazard Reduction from Earthquakes and Volcanoes in Indonesia 1992-2009 Researcher at Technology Center for Strength of Structures. Technology for the tests, analyses, evaluations, and retrofits of civil structures under earthquake and tsunami loadings
Selected Publications:
• “Toward an integrated tsunami disaster mitigation: Lessons learned from previous tsunami events in Indonesia,” JNDS, Vol.29, No.1, pp. 13-19, 2007. • “Tsunami mitigation efforts with pTA in West Sumatra Province,” Indonesia, Journal of Earthquake and Tsunami (JET), Vol.4, No.4, pp. 341-368, 2010. • “Examination of three practical run-up models for assessing tsunami impact on highly populated areas,” Natural Hazard and Earth System Science (NHESS), Vol.11, pp. 3107-3123, 2011.
Academic Societies & Scientific Organizations: • Japan Society of Civil Engineering (JSCE) • Japan Geophysical Union (JpGU)
Name: Erick Mas
Selected Publications:
• “Passively-controlled MR Damper in the Benchmark Structural Control Problem for Seismically Excited Highway Bridge,” Journal of Structural Control and Health Monitoring, Vol.16, Issue 6, pp. 626-638, 2009. • “Application of Angular-mass Dampers to Base-isolated Benchmark Building,” Journal of Structural Control and Health Monitoring, Wiley and Sons, Vol.15, Issue 5, pp. 737-745, 2007. • “Negative Stiffness Friction Damping for Seismically Isolated Structures,” Journal of Structural Control and Health Monitoring, John Wiley and Sons, Vol.13, pp. 775-791, 2006. • “Chapter 29 Seismic Base Isolation and Vibration Control,” Vibration and Shock Handbook, Editor: Clarence W. de Silva, CRC Taylor & Francis, June, pp. 29-1-29-75, 2005. • “Simple Algorithm for Semi-active Seismic Response Control of Cable-stayed Bridges,” Earthquake Engineering and Structural Dynamics, John Wiley and Sons, Vol.34, Issues 4-5, (10-25 April), 2005.
Academic Societies & Scientific Organizations: Affiliation:
• Japan Society of Civil Engineering (JSCE)
Doctoral Student of Tsunami Engineering, Disaster Control Research Center (DCRC), Graduate school of Engineering, Tohoku University
Name: Joachim Post
Address:
Affiliation:
Aoba 6-6-11, Sendai 980-8579, Japan
Scientist, German Aerospace Center (DLR), German Remote Sensing Data Center (DFD)
Brief Career: 1999-2004 B.S. Civil Engineering, National University of Engineering, Peru 2006-2009 M.S. Disaster Risk Management, National University of Engineering, Peru 2009- PhD Student, Tohoku University, Japan
Selected Publications:
• E. Mas, F. Imamura, and S. Koshimura, “Tsunami Hazard Mitigation and Countermeasures in Peru,” 3rd International Tsunami Field Symposium, April 9-16, 2010, Sendai, Japan. • E. Mas, F. Imamura, and S. Koshimura, “Tsunami Risk Perception Framework for the Start Time Evacuation Modeling,” XXV IUGG General Assembly International Association of Seismology and Physics of Earth’s Interior, June 28–July 7, 2011, Melbourne, Australia. • S. Koshimura, M. Matsuoka, M. Matsuyama, T. Yoshii, E. Mas, C. Jimenez, and F. Yamazaki, “Field Survey of the 2010 Tsunami in Chile,” Proc. of the 8th International Conference on Urban Earthquake Engineering, March 7-8, 2011, Tokyo, Japan. • A. Yalciner, A. Suppasri, E. Mas, N. Kalligeris, O. Necmioglu, F. Imamura, C. Ozer, A. Zaytsev, C. Synolakis, S. Takahashi, T. Tomita, and G. Yon, “Field Survey on the Coastal Impacts of March 11, 2011 Great East Japan Tsunami,” American Geophysical Union (AGU) Fall Meeting 2011, December 5–9, California, USA.
Address: Oberpfaffenhofen, 82234 Wessling, Germany
Brief Career: 2002-2006 Potsdam Institute For Climate Impact research (PIK), Germany 2006 PhD, University of Potsdam, Germany 2006- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Department of Civil Crisis Information and Geo-Risks
Selected Publications:
• “Assessment of human immediate response capability related to tsunami threats in Indonesia at a sub-national scale,” NHESS, Vol.9, pp. 1075-1086, 2009. • “Tsunami risk assessment in Indonesia,” NHESS, Vol.11, pp. 67-82, 2011. • “Examination of three practical run-up models for assessing tsunami impact on highly populated areas,” Natural Hazard and Earth System Science (NHESS), Vol.11, pp. 3107-3123, 2011.
Academic Societies & Scientific Organizations: • Japan Geoscience Union (JpGU)
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Name: Megumi Sugimoto
Affiliation: Earthquake Research Institute (ERI), The University of Tokyo
Address: 1-1-1, Yayoi Bunkyo-ku, Tokyo 113-0032, Japan
Brief Career: 2010- Researcher, ERI, The University of Tokyo 2005-2007 Program Officer, Economic Division, Embassy of Japan in Jakarta, Indonesia 2006-2009 Doctoral Candidate, Graduate School of Environmental Studies, Kyoto University, Japan
Selected Publications:
• “Learning from Earthquakes: The Japan Tohoku Tsunami of March 11, 2011,” Earthquake Engineering Research Institute (EERI) Report, November 2011, pp. 1-15, http://www.eqclearinghouse.org/2011-03-11sendai/files/2011/11/Japan-eq-report-tsunami2.pdf • “Tsunami height poles and disaster awareness: Memory, education and awareness of disaster on the reconstruction for resilient city in Banda Aceh, Indonesia,” Disaster Prevention and management, Emerald, Vol.19 No.5, pp. 527-540, 2010. • “Official development assistance of multi-disciplinary research for disaster management – Case study for earthquake, tsunami and volcano hazard in Indonesia –,” Environmental science, Soc. of Environmental Sci., Vol.23 No.6, pp. 537-541, 2010. • “Modeling and analysis of aid coordination processes for post – disaster education in Indonesia after the 2004 Indian Ocean Tsunami,” IEEE SMC, Texas, USA, pp. 1917-1922, 2009.
Academic Societies & Scientific Organizations:
• International Humanitarian Studies Association (IHASA) • American Geophysical Union (AGU) • International Union of Geodesy and Geophysics (IUGG) • Japan Society of Natural Disaster Science (JSNDS)
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