Landslide Susceptibility Mapping Using Artificial Neural Network in the ...

Landslide Susceptibility Mapping Using Artificial Neural Network in the Urban Area of Senise and San Costantino Albanese (Basilicata, Southern Italy) Stefania Pascale1, Serena Parisi1, Annagrazia Mancini1, Marcello Schiattarella2, Massimo Conforti3, Aurelia Sole1, Beniamino Murgante1, and Francesco Sdao1 1

School of Engineering, University of Basilicata, 85100 Potenza (PZ), Italy Department of Science, University of Basilicata, 85100 Potenza (PZ), Italy 3 CNR - Institute for Agriculture and Forest Systems in the Mediterranean (ISAFOM), Via Cavour 4/6, 87036 Rende (CS), Italy {Serena Parisi,parisiserena80}@yahoo.it 2

Abstract. Landslides are significant natural hazards in many areas of the world. Mapping the areas that are susceptible to landslides is essential for a wise territorial approach and should become a standard tool to support land-use management. A landslide susceptibility map indicates landslide-prone areas by considering the predisposing factors of slope failures in the past. In the presented work, we evaluate the landslide susceptibility of the urban area of Senise and San Costantino Albanese towns (Basilicata, southern Italy) using an Artificial Neural Network (ANN). In order, this method has required the definition of appropriate thematic layers, which parameterize the area under study. To evaluate and validate landslide susceptibility, the landslides have been randomly divided into two groups, each representing the 50% of the total area subject to instability. The results of this research show that most of the investigated area is characterized by a high landslide hazard. Keywords: Urban area, Basilicata, southern Italy, Artificial Neural Network, landslide susceptibility, land planning.

1

Introduction

The socio-economic impact of landslides in Italy is very important and makes our country is among the first in the world in the ranking of damage in both economic and loss of human lives terms. The landslides are among the natural disasters that occur with the greatest frequency and, after the earthquakes, the largest number of casualties and damage to population centers, infrastructure, historical and cultural heritage. As early as in 1910 [1] had highlighted, how the Basilicata was among the Italian regions most devastated by landslides due to climatic conditions, geomorphological and geolitological features, too. The local territory offers a very varied range of movements mass both for the combination of multiple causes and predisposing factor, and for the high incidence of destabilizing factors such as neotectonics, climate, B. Murgante et al. (Eds.): ICCSA 2013, Part IV, LNCS 7974, pp. 473–488, 2013. © Springer-Verlag Berlin Heidelberg 2013

474

S. Pascale et al.

anthropogenic and seismic activities. Landslides represent disasters with a major environmental damage. In the present work, the attention is focused on the spatial landslide hazard in particular or landslide susceptibility. Although, there are many definitions of hazard, as the probability that in a given area general conditions of stability can change suddenly towards instability. The landslide susceptibility or propensity to instability of the slopes, can be defined as the propensity of an area to trigger a landslide. For this reason the "susceptibility map" indicate mostly the geographical location of areas with different degrees of hazard [2]. The susceptibility prediction consists, in the evaluation of the relative hazard, or in the estimation of the instability slope degree with respect to another one, without expressing the probability of landslides occurrence in absolute or in a temporal terms. Although there are various criteria to assign the susceptibility, the literature distinguishes qualitative and quantitative methods. Among the quantitative methods, the neural analysis defined by the acronym ANN (Artificial Neural Network hereafter ANN), is a method widely used for the redaction of landslide susceptibility maps. The present work was aimed at the assessment of spatial landslide hazard map or susceptibility of the urban area of Senise and San Costantino Albanese (Basilicata, southern Italy) using the model based on artificial intelligence, such as Artificial Neural Networks.

2

Study Area

The Lucanian territory is a segment of the southern Apennines which represents a NE-verging orogenic wedge accreted from Late Oligocene—Early Miocene Pleistocene. The study area falls within the sheet 522 called SENISE of the Geological Map of Italy at 1:50,000 scale belonging to the new CARG project. The area is included in the tablets (1:25,000 scale) of “Senise” (F. 215 QII NO) and “San Costantino Albanese” (211 F. QII SO). It constitutes a portion of the southern sector of Basilicata and encompasses a wide and diverse sector of the Lucanian Apennines (Fig. 1). In the study area, it is possible to recognize various stratigraphic-structural units. The geological map includes both geological units of the meso-Cenozoic bedrock and Plio-Quaternary marine and continental successions. In particular, most of the units correspond to the Plio-Pleistocene clastic sedimentary successions of the southern Apennines chain outcropping in the southern sector of the Sant'Arcangelo basin Auctt., while more localized are the Quaternary continental deposits represented by terraced deposits. The Meso-Cenozoic bedrock includes low-grade metamorphic units, and both units of the crystalline basement and sedimentary successions. The geolithological features are widely reported in [3] and references therein included. The presence of various lithological units highlights a marked geomorphological diversity. The dynamic evolution of the landforms in the study area is mainly influenced by the outcropping rock types and their spatial distribution: lithologies are more resistant to erosion processes (selective erosion), in coincidence with outcrops of Mesozoic carbonates of shallow water, ofiolitifere metasedimentary successions and crystalline rocks give rise to morphologies bumpy and sometimes

Landslide Susceptibility Mapping Using Artificial Neural Network

475

harsh, while deposits of the Sant'Arcangelo basin with a dominant clay component, generate gentle landscapes generally with a low slopes. At the Plio-Quaternary sandyconglomerate clastic succession is observed a typical landscape in tabular reliefs, sometimes with selective erosion profiles that configure mesa type forms. This geological setting is reflected in the current modeling processes by exogenous agents that focus on other things being equal, with greater intensity in the areas most vulnerable to erosion. In particular, in the clay deposits are developed, with a certain frequency, badland forms. In addition to these, in the study area, are widely represented both current and relict river forms, as well as those related to landslides.

Fig. 1. Location of the study area in the geological context of the southern Apennines (Notes to the Geological Map of Italy at 1:50,000 scale, CARG Project - Institute for Environmental Protection and Research (ISPRA)

3

Production of the Thematic Data Layers

For the landslide-susceptibility mapping, the main steps were data collection and construction of a spatial database from which the relevant landslide conditioning factors are extracted, followed by assessment of the landslide susceptibility using the relationship between landslide and landslide-conditioning factors, and validation of the results. Landslides, in a strict sense, are movement of a mass of rock, debris or soil along a downward slope, due to gravitational forces. The inherent properties of the earth material, encompassing various geo-technical factors, make a particular area susceptible to landslides. A variety of movements are associated with landslides, such as flowing, sliding (translational and rotational), toppling or falling. Many landslides exhibit a combination of two or more types of movements resulting in a complex type [2]. In the present study the response variable is the landslide inventory map from an of the Senise and San Costantino Albanese areas that was represented in dichotomous form with 1 and 2 for “landslide” and “no-landslide”, respectively. Explanatory variables are the landslide influencing geo-environmental factor maps: Lithology; Land Use; Elevation; Angle of slope; Aspect; Plan curvature; Topographic Wetness Index. The landslide map was digitized and rasterized in 20×20 m grid. The other explanatory variables were rasterized to fit this grid size. The area grid was 546 rows by columns 933 with a total of 422.530 pixels.

476

3.1

S. Pascale et al.

Landslide Inventory Map

The landslide inventory has been created and managed within the GIS in the framework of a wider scientific research program carried out by several research units working at the School of Engineering of the University of Basilicata (Italy) under the project “Combined landslides and flood risk assessment along the road network of Potenza Province (Basilicata)”. A detailed landslide-inventory map of the study area (Fig. 2), was carried out integrating aerial photography interpretation acquired in 2010, 1:25000 scale topographic maps analysis, and with on extensive field survey. Mass movements are widespread in the study area, and play an important role in the present-day landscape evolution and are among the main geo-environment hazards. Many settlements and man-made infrastructures (e.g. roads and/or houses) are periodically damaged and/or destroyed by landslides activity. Acquired information have been implemented and archived on a GIS platform in order to obtain a georeferenced database, such as a landslide inventory. The whole landslide inventory is based on vector data, where landslide bodies are represented by closed polygons with attributes related to some of the fundamental parameters used in the description of the landslide body and possible landslide mechanisms. The landslide inventory map, defines the distribution of landslide area included in the Senise and San Constantino Albanese areas and also adopts a legend scheme distinguished by a different symbology for each "movement type" according to the classification of [4]. A total number of 470 landslides were detected an mapped in the study area (Fig. 2), which covered an area of 35.46 km2 and correspond to 17.4% of the total area. The minimum, mean and the maximum landslide areas are 0.006, 0.08 and 2.6 km2 respectively. From this study it was possible to recognize four types of landslides, which affecting the investigated area. The four landslides types are: flows (54 bodies, 11.50%), slides (238 bodies, 50.6%), complex landslides (170 bodies, 36.2%) and landslides widespread areas (8 bodies, 1.70%) (Fig. 2). On the basis of the obtained results, the geomorphological map has been redacted with the support of ArcGIS software version 10 (Fig. 2). 3.2

Choice and Classification of Preparatory Factors

The choice of the input parameters predisposing the landsliding of the study territory was carried out by considering the features of the study area and in relation to their influence on the mechanisms of the landslides processes [5], [6]. The methodology applied in this study is based on the well-known principle of “today and past are keys to the future” which is the fundamental principle of landslide susceptibility mapping studies. The selection of predisposing factors depends on the scale of analysis, the characteristics of the study area, the landslide type etc.. In this study, the most commonly used landslide predisposing factors in literature were selected to evaluation landslide susceptibility, but comparison between field investigations and landslide events contributed to it. In this study, we chose to represent each variable with a sequence of binary numbers, in order to avoid the introduction of diverse types of variables. The variables were subdivided into appropriate classes, defined on the basis

Landslide Susceptibility Mapping Using Artificial Neural Network

477

of the influence that they exert on landslide mechanisms [5], [6]. The basic themes, which are also the causes or factors of the distribution of landslides were obtained, with the aid of ArcGIS version 10, by the DEM (Digital Elevation Model) of the Senise and San Costantino Albanese areas with a resolution of 20m × 20m.

Fig. 2. Landslide inventory map of the area study

As below reported, seven parameters predisposing to landslides have been identified. These parameters considered in the present work as the input nodes, for the evaluation of landslide susceptibility were: Lithology (Litho); Land Use (Landuse); Elevation (DEMem); Slope angle (Slope); Slope Aspect (Aspect); Plan curvature (Curvature); Topographic Wetness Index (TWI). The variables were divided into classes defined by exercising influence on the mechanisms of the landslides. After being processed by Raster to ASCII data, they have been implemented and reclassified using IDRISI Taiga 2009 software, for the preparation of the maps for each parameter to be included in the Multi-Layer Perceptron (MLP) module, used to generate the Artificial Neural Network. Below the adopted parameters are reported. The DEM represents the spatial variation of elevation over the area. Contours and survey base points, that have an elevation value, were extracted from the topographic map. A digital elevation model (DEM) was constructed by using the inverse distance weighting (IDW) method of interpolation with 20x20 m resolution. The accuracy of DEM was quantitatively verified on the basis of several field-surveyed points, by using GPS and total station points. In the study area the elevation varies between 220 and 1584 m (Fig. 3 and 5). A total of 61.9% of the landslides occurred in areas with an elevation between 200 and 600 m upon the sea level. Slope: through the morphological analysis of the digital elevation model it is possible to perform a parameterization of the surface, the purpose of which is the numerical description of the shape of the

478

S. Pascale et al.

continuous surface. Slope angle is very regularly used in landslide susceptibility studies since landsliding is directly related to slope angle, [9], [8] . The slope map of the study area was divided into 7 slope categories, depending on considerations derived from the literature and a series of preliminary statistical analysis based on crossing between the database of collapse landslide areas and maps of the slopes. Figure 3 shows the map of the gradients derived from the DEM. The slope angle of the area ranges from 0° degrees to as much as 86° degrees and most of the recognized landslides (61%) fall into the classes between 0 – 5°, 5° - 10° and 10° – 15° (Fig. 3 and 5). Aspect: Aspect associated parameters such as, exposure to sunlight, drying winds, rainfall (degree of saturation), and discontinuities may affect the occurrence of landslides [8], [9]. [10]. Similar to slope, an aspect thematic data layer was derived from the DEM also, which represent five aspect categories (Fig. 3 and 5): flat (-1°), north (0°-45°, 315°-360°), east (45°-135°), south (135°-225°), west (225°-2315°). Curvature: the term curvature is theoretically defined as the rate of change of slope gradient or aspect, usually in a particular direction [11]. Plan curvature is described as the curvature of a contour line formed by intersecting a horizontal plane with the surface. The influence of plan curvature on the slope erosion processes is the convergence or divergence of water during downhill flow. For this reason, this parameter constitutes one of the conditioning factors controlling landslide occurrence [12]. The plan curvature profile was divided into three classes: concave, convex and nothing. Positive curvature values express a convexity of the slope, negative values a concavity of the slope; values near to zero indicate the presence of an inflection, that is, a surface or concave or convex. Lithology: landslides are greatly controlled by the lithology properties of the land surface. For this reason, it is essential to properly group the lithologic properties [13], [14], [15], [9]. Therefore, a lithology map of the study area is digitized from the existing geology map (Geological Map of Italy, Sheet 522 "Senise") at the scale of 1:50,000 from the Geological Survey of Italy, by integrating air photo interpretation and field data survey. The general geological setting and the lithological properties of the area are summarized in Figures 3 and 5. Land use: the covering soil map (land cover) is prepared for a physical description of the area, at the confluence of multiple environmental factors of geomorphological, pedological and vegetation characters, both natural and human, also including changes due to the urbanization, agriculture and livestock activities. The combination of all these factors contribute to modeling territory with variable weight, according to different modes of cause and effect. The used covering soil map is produced for the CORINE Land Cover project [16] (Fig. 3 and 5). TWI: the topographic wetness index (TWI) has been used extensively to describe the effect of topography on the location and size of saturated source areas of runoff generation. [17] proposed the equation below reported for the calculation of TWI under the assumption of steady state conditions

Fig. 3. Input data layers (a) Lithology; (b) Land use; (c) Aspect; (d) Elevation; (e) Plan curvature; (f) Slope; (g) Topographic wetness index (TWI)

Landslide Susceptibility Mapping Using Artificial Neural Network 479

480

S. Pascale et al.

and uniform soil properties (i.e. transitivity is constant throughout the catchment and equal to unity):

TWI = ln( As / tan σ ) where As is the specific catchment’s area (m2/m), and (Fig. 3 and 5)

4

Methodology: Modeling Approach

4.1

Artificial Neural Network Model

(1)

σ

is slope gradient(in degrees)

Susceptibility levels can be evaluated and expressed in different ways. Conventional methods of landslide susceptibility assessment may be classified into four categories: landslide inventories, heuristic methods, statistical methods and deterministic approaches. Specific details about these methods can be found in [18]. Landslide inventories and heuristic methods concern the production of landslide hazard maps based on the knowledge of experts. The main limitation of these methods is their subjectivity. The statistical methods and deterministic approaches are formally more rigorous. The main limitation of deterministic approach is that its correct application requires detailed geotechnical and hydrological data that are difficult to acquire on wide areas. This method can be applied to a single-slope scale. Statistical methods guarantee a lower subjectivity, but they are more easily applied to areas characterized by a unique type of mass movements. The proposed model presented in the present work is based on the neural

analysis technique that can be considered as a quantitative method and a black-box model [18]. An important advantage of the AAN method is that it is independent from the statistical distribution of the data and there is no need for specific statistical variables [19]. Compared with statistical methods, the neural methods permit the target classes to be defined, taking into account their distribution in the corresponding domain of each data source [19], [5], [6] and [20]. The resulting information can then be used in the prediction of areas that may face landsliding in the future. ANNs are generic non-linear function approximators that were developed by [21] and extensively used for pattern recognition and classification. Artificial Neural Networks are computational networks which attempt to simulate, the networks of nerve cell (neurons) of the biological central nervous system [22]. The connections in ANN is simulated by a weight ass most commonly used ANN models is the feed forward back-propagation ANN. This is a supervised, pattern recognition model that needs to be trained using a data set for which both the input values (x) for a set of predictors and the correct output values (y) are known for a set of examples. The architecture of this ANN is based on a structure known as the Multi-Layer Perceptron (MLP). The MLP consists of a set of layers, each of which is composed of a set of nodes (alternatively referred to as “units” or “neurons”). The MLP with the backpropagation algorithm is trained using a set of examples of associated input and output values [22] . The purpose of an artificial neural network is to build a model of the data-generating process, so that the network can generalize and predict outputs

Landslide Susceptibility Mapping Using Artificial Neural Network

481

from inputs that it has not previously seen. A network has two use modes: learning and recall. Upon receiving sufficiently intense stimulus (input) from the preceding units, the unit is activated and sends signal to the connecting units. The above mentioned neurons thus elaborate the stimulation they receive. The transformation is completed in two phases: firstly, each input signal is multiplied by the weight of the connection and the results of the single products are added to obtain an amount called total input; secondly, the unit applies a transfer function which transforms the sum of the input signals into output signals. Learning: this phase consists in providing a network which has initially random connection weights with a set of stimulus couples called learning examples which represent the input to the network and the expected output. This procedure is usually carried out by means of the learning algorithm called Back propagation [22]. Recall: allows for the application of the synthesized knowledge from the preceding learning phase. In a successive phase the neural network is able to provide coherent answers to input which is not presented in the training phase. 4.2

Validation Procedure

Very often in prediction modelling, the most important phase is the validation of the prediction results. In fact, without the validation procedure, the prediction model and image are completely useless and have hardly any scientific importance. In the present paper we present several, rather simple, procedures for the validation, so that the prediction results can be interpreted significantly with respect to the future landslide hazard. After a prediction image is obtained, the proper validation should be based on the comparison between the prediction results and the unknown target pattern of the areas affected by future landslides. It is to consider that the investigated area is characterizes by the presence of old existing landslides. Due to the fact that the target pattern (the future landslides) is unknown, a direct comparison with the target pattern is not possible. The next best way to do is to imitate the comparison by using a part of the past landslides as if it represents the target pattern. To mimic the comparison, it is need to restrict the use of all the data of the past landslides in the area under examination. Once the data are partitioned, one subset of data is used for obtaining a prediction image; the other subset is compared with the prediction results for validation. By assuming that the two data sets are not coincident, we can assume that, if the model is adequately to describe the data validation, then it is able to correctly reproduce the real system. The validation set is then to decide which type of model is the best. The diagnosis of the training sample and the validation of the test sample were assessed for each model with the construction of the confusion matrix and the ROC curve. Validation of landslide susceptibility maps is commonly based on: the confusion matrix or contingency table [23]. Based on a threshold, continuous susceptibility values are categorized in a binary map (susceptible and not susceptible classes) and then compared with a binary landslide distribution map (presence or absence of landslides). The confusion matrix consists in the calculation of overlap areas between the two binary maps. The possible combinations are: landslide areas are classified as

482

S. Pascale et al.

susceptible areas (true positive observations); landslide-free areas are classified as no susceptible areas (true negative observations); landslide areas are classified as no susceptible areas (false negative observations); and landslide-free areas are classified as susceptible areas (false positive observations) as shown in Table 1. The confusion matrix provides the accuracy of the obtained classification. The confusion matrix was calculated by comparing the location and class of each ground truth pixel with the corresponding location and class in the obtained classified image. The overall accuracy was calculated by summing the number of pixels classified correctly and dividing it by the total number of pixels. Table 1. Confusion matrix. a, true positive observations; d, true negativeobservations; b, false negative observations (error type II); and c, false positiveobservations (error type I) Source Begueria (2006)

PREDICTED X1’ X0’

OBSERVED X1 X0 a b d c

A1n alternative to the above statistics that depend on the threshold (cut-off value) for their calculations is the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) [24].This method is already widely used as a measure of performance of a predictive rule.. ROC plots the different accuracy values obtained against the whole range of possible threshold values of the functions, and the AUC serves as a global accuracy statistic for the model, regardless of a specific discriminant threshold. This curve is obtained by plotting all combinations of sensitivities and proportions of false negatives (1-specificity) which may be obtained by varying the decision threshold. The range of values of the ROC curve area is 0.5–1 for a good-fit, while values below 0.5 represent a random fit [24].

5

Results and Discussions: Landslide Susceptibility by Artificial Neural Networks

5.1

Development of ANNs

The probabilities of landslides occurrence have been calculated based on the different input attributes that have been listed in Figure 3 and knowledge based classification. Prior to running the artificial neural network program, the training site should be selected. So, the landslide-prone (occurrence) area and the landslide-not-prone area have been chosen as training sites. Pixels from each of the two classes have been randomly selected as training pixels, having 43,418 pixels denoting areas where landslide not occurred or occurred. The areas where the landslide was not occurred were classified as “areas not prone to landslide” while, areas where landslide was known to exist were assigned to an “areas prone to landslide” training set (Fig. 4). Training sites have been selected based on landslide location as prone training site

Landslide Susceptibility Mapping Using Artificial Neural Network

483

and with a varying slope values as non-prone training site and then the MLP trained back propagation algorithm has been computed. This module uses back propagation (BP), the learning algorithm which allows the modification of connection weights so as to minimize function error E. The phase concerning the updating of weights represents the training step of the neural network. In order to train the network, the landslide inventory map was reclassified by assigning a value of 1 to the landslide pixels and a value of 2 to the not-landslide pixels. Some of the input attributes layers are continuous and others categorical in nature. Therefore, these data were converted to raster grid in order to apply the ANN model, with input data normalized in the range 0-1. A three-layer feed-forward network consisting of an input layer (7 neurons), one hidden layer (15 neurons) and one output layer was used as a network structure of 7-15-1 (Fig. 4). The neural network is made up by adjusting several parameters including: the number of hidden nodes, the training pixels per category, the momentum factor coefficient, the learning rate, the number of training cycles (iterations) and the Root Mean Square Error (RMSE). The learning rate is a constant, controlling the adjustment of the weights associated with the connections. If these are too small, the training phase can be overestimated. At the contrary, if they are high, it can be underestimated. The momentum factor prevents problems of divergence during research for minimum errors, and has been used to accelerate convergence. From each of the 2 classes (landslide and not-landslide), 2,000 pixels per class were selected as training pixels at random, as we proved that the number of training locations did not influence the analysis. The learning rate was set to 0.01 and the momentum factor was set to 0.5. The maximum numbers of iterations have been set to 10,000. The root mean square error (RMSE) value used for the stopping criterion was set to 0.01. When the latter case occurred, then the maximum RMSE value was 0.283 and produced a training overall accuracy of 79.2%. Finally, the landslide susceptibility maps have been generated (Figure 7). The susceptibility values show a minimum of 0 and a maximum of 0.999, with an average value of 0.428 and a standard deviation of 0.300. The numbers close to 1 indicate a greater likelihood of slope instability whereas numbers close to 0 show a low probability. The susceptibility values for each cell have been converted to raster format file in a GIS platform and the final landslide susceptibility map was produced (Fig. 6). The pixel values were classified into five susceptibility classes: very low, low, moderate, high and very high. Given this classification, more than of the 30.5% of the study area falls in high to very high susceptibility classes and most of the mapped landslides (more than of the 65%) occur in these classes. In particular, landslide susceptibility is very high in the high relief of the middle-southern portions of the study area; this result is in agreement with the concentration of more than 60% of the landslide mapped on this areas. In addition, the flyschoid complex, were found to be most prone to slope instability. On the contrary, low and very low landslide susceptibility, which covers about 57,1% of the investigated area, occurs where slope gradient ranges between 0° and 10° (flat or gently sloping land surfaces), TWI values are high (>2) and the sandstone and alluvial complex outcrop. A comparison between the susceptibility map (Fig. 6) and the landslides inventory map (Fig. 2), show more than of 79.2% of the overall landslides data set have been correctly classified, falling in high and very high susceptibility classes.

484

S. Pascale et al.

Fig. 4. Training set and testing set of the area study

5.2

Validation of the Landslide Susceptibility Maps

The result obtained by the landslide susceptibility analysis has been verified using landslide test locations which were not used during the modelling process. The accuracy of the predictive model has been assessed thanks to the use of the confusion matrix and the ROC curve analysis [26], [23], [6], [27], [28] and [29]. Also the validation of the susceptibility map was performed using a confusion matrix. The results of the ANNs model show an overall accuracy of 79.2%. The observed and predicted accuracy are shown in Table 2. It is important denote that the predicted accuracy, is a measure indicating the probability that the classifier has labeled an image pixel into Class A, when the ground truth is Class A. The results reported in Table 2 show that predicted accuracy is 81.7% for landslide cells and 76.7% for nonlandslide cells. In particular, the measure of the goodness of fit of the model, representing the correlation between the testing set (used in the neural network analysis) and the final landslide susceptibility model, has been considered satisfactory because, when the susceptibility value of 0.5 was used as the cut-off value, 79.2% of the landslide testing set were correctly classified.

Landslide Susceptibility Mapping Using Artificial Neural Network

485

Table 2. Confusion matrix of the testing set (cut-off value: 0.5) Observed

Predicted Non-occurrence landslide pixels

% correct

Landslide pixels Landslide pixels Non-occurrence landslide pixels Overall accuracy

35895

8039

81.7

10206

33581

76.69 79.2

The acceptability of susceptibility map produced from the proposed approach may further be strengthened through ROC curves. With reference to the landslides occurrence, sensitivity or the true positive rate is the probability that a slided cell is correctly classified and is plotted on the Y-axis in the ROC curve. Specificity or the true negative rate is the probability that a non-slided cell is correctly classified. The false positive rate is obtained by subtracting the true negative rate from 1 and is taken as the X-axis of the curve. The ROC curves can be summarized quantitatively with the help of the area under the ROC curve, which will give the accuracy of the developed model for predicting the landslide susceptibility. The 43,934 landslide cells which have not been used in the Artificial Neural Network analysis, together with an equal number of randomly selected non-landslide cells, have been considered for the ROC curve analysis. A tabular data with two columns (one column containing the landslide status and the other with the estimated probability of slope failure) has been prepared for these 87,868 cells and has been considered as the input table for ROC analysis. The ROC curve for the ANN model used in the proposed study is given in Fig. 5. The area under the ROC curve is 0.75.

Fig. 5. ROC curve for testing set of the Artificial Neural Network model. The area under the ROC curve (AUC) is 0.75.

486

S. Pascale et al.

Fig. 6. Landslide susceptibility map of the study area produced by using the Artificial Neural Network (ANN)

6

Conclusions

In the presented work, we evaluate the spatial landslide hazard of the urban area of Senise and San Costantino Albanese towns (Basilicata, southern Italy). The chosen area, was initially elaborating the landslides inventory map After to the phase of census landslides, it has been implemented the Artificial Neural Network (ANN), using the module Multi-Layer Perceptron (Mlp) of the IDRISI Taiga 2009 software [25]. This analysis was chosen among the various methods present in the literature, because it is a quantitative statistical and innovative technique. In particular, the application of ANN to the case study, has been divided into four main phases: a) an initial phase in which they were selected as input parameters of the network, the factors predisposing to landslides of the area; b) a second phase, called training, in which have been taken into account 50% of the areas in landslides and not in landslide, representing the training phase of the network; c) a third step, consisting in the elaboration of the landslide susceptibility map, allowed the identification of five principal classes: very low, low, medium, high, and very high; d) a four step, the validation of the procedure, in which the map of landslide susceptibility processed by ANN has been validated with the testing. The validity of the obtained results was confirmed by performing the validation procedure, reaching an overall accuracy of 79.2%.

Landslide Susceptibility Mapping Using Artificial Neural Network

487

The developed procedure shows that the artificial neural network, has produced a map of landslide susceptibility that is representative of the real situation of the study area, in fact most of the surveyed landslides, fall in the areas characterized by high and very high susceptibility. Based on various works taken into account, we can say that the neural network could be a relatively simple solution to solve complex problems including those relating to the estimation of landslide susceptibility. In summary it can be stated that the ANN, thanks to its dynamic characteristics, flexible and non-linear adaptability, offers the advantage of solving complex relations of input-output. The result of the proposed approach could be a useful tool for decision support planners in areas prone to landslides and beyond, in order to reduce and prevent the loss of function of the area affected by the natural disaster. The maps of susceptibility landslide could be a useful information tool for engineering applications and for planning, too.

References 1. Almagià, R.: Studi Geografici sopra le frane in Italia. In: L’Appennino centrale e meridionale - conclusioni generali, vol. II, 14, p. 435. Società Geografica Italiana, Roma (1910) 2. Varnes, D.J.: International Association of Engineering Geology Commission on Landslides and Other Mass Movements: Landslide hazard zonation: A review of principles and practice. In: Natural Hazards, vol. 3, p. 63. UNESCO, Paris (1984) 3. Schiattarella, M., Giannandrea, P., Giano, S.I., Pinto, F.: Note illustrative della Carta Geologica d’Italia alla scala 1:50.000 foglio 522 Senise (2011) 4. Cruden, D.M., Varnes, D.J.: Landslides types and processes. In: “Landslides: investigation and mitigation”. Spec. Rep. 176, Transp. Res. Board, Nat. Ac. of Sc., Washington (1994) 5. Caniani, D., Pascale, S., Sdao, F., Sole, A.: Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Natural Hazards, Springer ed. 45, 55–72 (2008) 6. Sdao, F., Lioi, D.S., Pascale, S., Caniani, D., Mancini, I.M.: Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Nat. Hazards Earth Syst. Sci. 13, 395–407 (2013), doi:10.5194/nhess-13-3952013 7. Dai, F.C., Lee, C.F., Li, J., Xu, Z.W.: Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ. Geol. 40, 381–391 (2001) 8. Cevik, E., Topal, T.: GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ. Geol. 44, 949–962 (2003) 9. Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., Reichenbach, R.: Comparing landslide inventory maps. Geomorphology 94, 268–289 (2008) 10. Komac, M.: A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4), 17–28 (2006) 11. Wilson, J.P., Gallant, J.C.: Terrain analysis principles and applications. Wiley, New York www.em-dat.net EM-DAT: The OFDA/CRED International Disaster Database. Universite Catholique de Louvain, Brussels, Belgium (2000)

488

S. Pascale et al.

12. Nefeslioglu, H.A., Gokceoglu, C., Sonmez, H.: An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology 97, 171–171 (2008) 13. Mejia-Navarro, M., Garcia, L.A.: Natural hazard and risk assessment using decision support system, application: Glenwood Springers, Colorado. Environ. Eng. Geosci. 2(3), 299–324 (1996) 14. Pachauri, A.K., Gupta, P.V., Chander, R.: Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology 36, 325–334 (1998) 15. Luzi, L., Pergalani, F.: Slope instability in static and dynamic conditions for urban planning: the “Oltre Po Pavese” case history (Regione Lombardia-Italy). Natural Hazard 20(1), 57–82 (1999) 16. Cumer, A.: Il progetto CORINE Land Cover in Italia: un modello da seguire. Documenti del Territorio Anno VIII N. 28/29 (1994) 17. Moore, I.D., Grayson, R.B., Ladson, A.R.: Digital terrain modelling: a reviewof hydrological, geomorphological, and biological applications. Hydrological Processes 5, 3–30 (1991) 18. Aleotti, P., Chowdhury, R.: Landslide hazard assessment: summary review and new perspectives. Bull. Eng. Geol. Environ. 58, 21–44 (1999) 19. Lee, S., Ryu, J.H., Won, J.S., Park, H.J.: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng. Geol. 71, 289–302 (2004) 20. Bishop, C.M.: Neural Networks for Pattern Recognition, 1st edn. Oxford University Press, USA (1996) 21. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943) 22. Turban, E., Aronson, J.E.: Decision Support Systems and Intelligent Systems. In: Graupe, D. (ed.). Prentice Hall (2001); Principles of Artificial Neural Networks. World Scientific Publishing (2001) 23. Bonham-Carter, G.F., Agterberg, F.P., Wright, D.F.: Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg, F.P., Bonham-Carter, G.F. (eds.) Statistical Applications in the Earth Sciences, Geological Survey of Canada, pp. 171–183 (1994) 24. Zweig, M.H., Campbell, G.: Receiver-operating characteristics(ROC) plots. Clinical Chemistry 39, 561–577 (1993) 25. Eastman, J.R.: Idrisi Taiga, guide to GIS and image processing, user’s guide (Ver. 15), p. 328. Press Clark University, USA (2009) 26. Swets, J.A.: Measuring the accuracy of diagnostic systems. Science 204, 1285–1293 (1988) 27. Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006) 28. Conforti, M., Robustelli, G., Muto, F., Critelli, S.: Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria, south Italy). Natural Hazards 61, 127–141 (2012) 29. Lucà, F., Conforti, M., Robustelli, G.: Comparison of GIS based gullying susceptibility mapping using bivariate and multivariate statistics in Northern Calabria (South Italy). Geomorphology 134, 297–308 (2011)