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International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland
Firemap: A Dynamic Data-Driven Predictive Wildfire Firemap: A Data-Driven Predictive Firemap: A Dynamic Dynamic Data-Driven Environment Predictive Wildfire Wildfire Modeling and Visualization Firemap: A Dynamic Data-Driven Predictive Wildfire Modeling Environment Modeling1∗ and and Visualization Visualization Environment Daniel Crawl , Jessica Block2 , Kai Lin1 , Environment and Ilkay Altintas1 Modeling and Visualization Daniel Crawl1∗, Jessica Block2 , Kai Lin1 , and Ilkay Altintas1 1∗ 2 1 Daniel Crawl , Jessica Block , University Kai Lin1of , and Ilkay San Altintas 1 San Diego Supercomputer Center, California, Diego 1 1∗ 2 1 1 Daniel Crawl , Jessica
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Abstract Abstract Wildfires are destructive fires over the wildland that can wipe out large areas of vegetation Abstract Wildfires are destructive fires thetowildland that manage can wipeasout large of vegetation and infrastructure. Such fires areover hard control and they canareas change directions Abstract Wildfires are destructive fires over thetowildland that manage can wipeasout large areas of vegetation and infrastructure. Such fires are hard control and they can change directions almost instantly, driven by changing environmental conditions. Effective response to such events Wildfires are destructive fires thetowildland that manage can wipeasout large of vegetation and infrastructure. Such fires areover hard control conditions. and they canareas change directions almost instantly, driven by changing environmental Effective response to such events requires the ability to monitor and predict the behavior of the fire as fast as they change. and infrastructure. Such are hard to control conditions. and manageEffective as theyresponse can change directions almost instantly, driven byfires changing environmental to such events requires the ability to monitor and predict the behavior of the fire as fast as they change. The WIFIRE project builds an end-to-end cyberinfrastructure for real-time and data-driven almost instantly, driven by changing environmental conditions. Effective response to suchchange. events requires the ability to monitor and predict the behavior of the fire as fast as they The WIFIRE project builds an end-to-end cyberinfrastructure for real-time and isdata-driven simulation, and visualization of wildfire behavior.ofOne goal of to change. provide requires theprediction, ability tobuilds monitor and predict the behavior thefor firereal-time as WIFIRE fast and as they The WIFIRE project an end-to-end cyberinfrastructure data-driven simulation, and visualization of wildfire behavior. One goal ofthis WIFIRE is to provide the toprediction, predict more accurate rate ofcyberinfrastructure a spreading wildfire. To end, has The tools WIFIRE projecta builds an end-to-end real-time andWIFIRE simulation, prediction, and visualization of wildfire behavior. Onefor goal ofthis WIFIRE isdata-driven to provide the tools to predict a more accurate rate of a spreading wildfire. To end, WIFIRE has developed interfaces for ingesting and visualizing high-density sensor networks to improve fire simulation, prediction, and visualization of wildfire behavior. One goal ofthis WIFIRE is to provide the tools to predict aformore accurate rate of a spreading wildfire. Tonetworks end,toWIFIRE has developed interfaces ingesting and visualizing high-density sensor improve fire and weather predictions, and has created a data model for wildfire resources including sensed the tools to predict aformore accurate rate of a spreading wildfire. this end,toWIFIRE has developed interfaces ingesting and visualizing high-density sensorTo networks improve fire and archived weather predictions, and has created a data model for wildfire resources including sensed data, sensors, satellites, cameras, modeling tools, workflows, and social information developed interfaces for ingesting and visualizing high-density sensorresources networks to improve fire and weather predictions, and has created a data model for wildfire including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows, and social information including Twitter feeds for wildfire research andmodel response. This paper presents WIFIRE’s weather predictions, and has created a data for wildfire resources including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows, and social information including Twitter feedstofor wildfire research and response. This accessible. paper presents WIFIRE’s Firemap web platform make thesecameras, geospatial data and products Through a web and archived data, sensors, satellites, modeling tools, workflows, and social information including Twitter feedstofor wildfire research and response. This accessible. paper presents WIFIRE’s Firemap web platform make these geospatial data and products Through a web browser, Firemap enables geospatial information visualization and a unified access to geospatial includingweb Twitter feedstofor wildfire research and response. This accessible. paper presents WIFIRE’s Firemap platform make theseinformation geospatial data and products Through a web browser, Firemap enables geospatial visualization and a unified access to geospatial workflows using Kepler.toUsing capabilities scalable big data Through integration and Firemap Firemap web platform makeGIS these geospatialcombined data andwith products accessible. a web browser, enables geospatial information visualization and a unified access to geospatial workflows using Kepler. Using GIS capabilities combined with scalable bigfordata integration and processing, Firemap enables simple execution of the model with options running ensembles browser, Firemap enablesUsing geospatial informationcombined visualization and a unified access to geospatial workflows using Kepler. GIS capabilities with scalable bigfordata integration and processing, Firemap enables simple execution of the model with options running ensembles by taking the information uncertainty into account. Thewith results are easily viewable, sharable, workflows using Kepler. Using GIS capabilities combined scalable bigfordata integration and processing, Firemap enables simple execution of the model with options running ensembles by taking the uncertainty into series. account. The results are easily viewable, sharable, repeatable, andinformation can be animated as execution a time processing, Firemap enables simple of the model with options for viewable, running ensembles by taking the information uncertainty into account. The results are easily sharable, repeatable, and can be animated as a time series. by taking the uncertainty into series. account. The results are easily viewable, sharable, Keywords: wildfire, visualization, Kepler, GIS repeatable, andinformation can be animated as B.V. a time © 2017 The Authors. Published by Elsevier Keywords: wildfire, Kepler, GIS repeatable,under andresponsibility canvisualization, be animated as a time series. Peer-review of the scientific committee of the International Conference on Computational Science Keywords: wildfire, visualization, Kepler, GIS Keywords: wildfire, visualization, Kepler, GIS
1 Introduction 1 Introduction 1 Introduction Wildfires are large destructive fires, often in the wildland urban interface, that can wipe out 1 Introduction Wildfires destructive fires, often inSuch the wildland urbantointerface, that can wipe out large areasare of large vegetation and infrastructure. fires are hard control and manage as they
Wildfires destructive fires, often inSuch the wildland urbantointerface, that can wipe out large areasare of large vegetation and infrastructure. fires are hard control and manage as they ∗ Correspondence Wildfires destructive fires, often in the wildland urban interface, that can wipe out large areasare of large vegetation and infrastructure. Such fires are hard to control and manage as they should be sent to:
[email protected]. This work was primarily supported by NSF-1331615 ∗ Correspondence under CI, Information Technology Research and SEES Hazards programs. should be sentinfrastructure. to:
[email protected]. This work was primarily supported by NSF-1331615 large areas of vegetation and Such fires are hard to control and manage as they ∗ Correspondence should be sentResearch to:
[email protected]. This work was primarily supported by NSF-1331615 under CI, Information Technology and SEES Hazards programs. ∗ Correspondence under CI, Information Technology and SEES Hazards programs. should be sentResearch to:
[email protected]. This work was primarily supported by NSF-1331615 1 under CI, Information Technology Research and SEES Hazards programs.
1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science 10.1016/j.procs.2017.05.174
1 1 1
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can change directions almost instantly, driven by changing environmental conditions. Effective response to such events requires the ability to monitor and predict the behavior of the fire as fast as they change. WIFIRE [3, 29] builds an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction, and visualization of wildfire behavior. One goal of WIFIRE is to provide the tools to predict a more accurate rate of a spreading wildfire. To this end, WIFIRE has developed interfaces for ingesting and visualizing high-density sensor networks to improve fire and weather predictions, and has created a data model for wildfire resources including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows, and social information including Twitter feeds for wildfire research and response. In order to make these geospatial data and products accessible, WIFIRE developed geospatial workflows enabled in a web browser platform called Firemap [31] using the Kepler Scientific Workflow System [25, 2]. Using GIS capabilities combined with scalable big data integration and processing, Firemap enables simple execution of the model with options for running ensembles by taking the information uncertainty into account. The results are easily viewable, sharable, repeatable, and can be animated as a time series. Firemap provides a simplified, yet scalable and extensible mapping and visualization platform for wildfire information dissemination and near real-time predictive fire behavior modeling, making the WIFIRE cyberinfrastructure useful to their partners in city, county, and state fire agencies who are using these tools for daily operations. This paper presents the Firemap architecture and its main components for geospatial data services, predictive fire modeling and web-based visualization. We believe that Firemap is the first extensible tool that provides system integration of diverse fire datasets (ranging from ground-based sensors, mountain-top cameras, satellites, and public databases for land management) and makes them integrated into predictive fire modeling in a near real-time fashion. The rest of this paper is organized as follows. Section 2 provides a summary of related work and comparisons to Firemap. Section 3 describes the Firemap architecture, which includes the Firemap interactive web application and Pylaski, GeoServer, Kepler WebView, and LiveWx data services. Section 4 concludes the paper and presents some of our future work plans.
2
Related Work
The AEGIS wildfire management information system is most similar to Firemap in that it deploys a web-based GIS to ingest data and predict fire behavior in real-time [10]. Firemap differs in its ability to run ensembles of fire propagation with multiple options for editing the weather inputs: either manually, using time-stepped weather forecast, or constant weather conditions from the nearest weather station. Firemap also allows for ingesting a data service for real-time reports of updated fire ignitions or perimeters that can be used to update the fire modeling. The PHOENIX RapidFire model is used to show the likelihood of the fire spread under different weather and fire suppression scenarios using propagation equations that best approximate fire behavior in Australia [30]. It is capable of being run and rerun in minutes with updated information as it is available. Unlike Firemap, PHOENIX RapidFire is not web-enabled and does not currently have mechanisms for running ensembles. FlamMap computes fire behavior characteristics spatially for constant environmental conditions. Instead of depicting fire spread over time, it provides a comparison of fire potential fire behavior over the landscape and can be used for determining best fuel treatment scenarios [8]. FARSITE is a desktop application used most widely for training and for operations 2
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during large wildfire events by fire agencies across the US including CAL FIRE and US Forest Service [6] . It uses a semi-empirical model to calculate fire growth in deterministic simplified test conditions, incorporating existing fire behavior formulas of surface fire spread, crown fire spread, spotting, point source fire acceleration, and fuel moisture. The model produces 2D vector fire perimeters at specified time intervals [7]. FARSITE can help analysts determine the rate and direction of spread of a particular fire and predict when it will reach a given location. The vector propagation technique allows the model to be adaptive to changes in wind speed and direction. Although input files required to run FARSITE are available for free download from publicly available websites (National Weather Service, USGS, LANDFIRE, MesoWest), the formatting and ingestion of those data to FARSITE is cumbersome, and does not ingest real-time data. The Wildland Fire Decision Support System (WFDSS) is a web-based fire management decision support platform used for large fires across the United States [18]. Access is given only to approved and trained fire responders. There are many components to WFDSS for the decision support of the fire incident including the fire analysis modules. The Basic Fire Behavior model is a version of FlamMap, the Short-Term Fire Behavior calculates the minimum travel time of a fire, and the Near-Term Fire Behavior is a version of FARSITE. FSPro is a program created by the US Forest Service that calculates the spatial probability of fire spread using uncertainty in the weather. It is used to determine a fire’s growth potential from an active fire perimeter, to determine priorities for firefighting resources [16]. It generates hundreds of potential wind and weather scenarios (based on the current season’s weather as well as historic weather) and incorporates this information in simulating thousands of individual fires. Although the results of FSPro runs can be improved with weather sensors that are in better locations and more densely distributed, FSPro is a probability tool, and uses a large historic dataset to create those probabilities. Due to this, FSPro is not useful for ingesting real-time weather data. SCOUT (previously National Incident Command System) was developed by the MIT Lincoln Laboratory and is now managed by the California Office of Emergency Services (CalOES) [20]. It is a field incident management system for any disaster response scenario, including wildfires. Although users can manage incident information, there are no simulation modules within it. Once calculated outside of SCOUT, managers can add tactical decisions from their modeling into it. Users can view current weather conditions with other conditions including road conditions, utilities, census information, known hazards, and government boundaries. It is not a system designed to ingest real-time environmental data for analysis.
3
Architecture
The WIFIRE architecture is shown in Figure 1. An interactive map application called Firemap runs in clients’ web browsers and communicates with four services: Pylaski, GeoServer, Kepler WebView, and LiveWx. The rest of this section describes each of these components.
3.1
Firemap
The Firemap user interface, or Firemap, is an interactive graphical application that allows users to perform predictive wildfire modeling and visualize WIFIRE geospatial datasets. The primary interface is a tiled web map that users can zoom and pan in the web browser. WIFIRE datasets are visualized as a set of layers that can individually be added to or removed from the map. A layer’s transparency can also be changed allowing better insights of spatially overlapping datasets. Figure 2 shows several example layers: (a) the Weather Stations Layer 3
Daniel Crawl et al. / Procedia Computer Science 108C (2017) 2230–2239 Firemap: A Dynamic Data-Driven Predictive Wildfire Modeling and. . . Clients
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Figure 1: WIFIRE architecture comprised of Firemap user interface, Pylaski, GeoServer, Kepler WebView, and LiveWx services, and local and external data sources. showing weather stations throughout in San Diego County, (b) the VIIRS Layer showing satellite detections of the Rey Fire, (c) the Historical Fires Layer showing past wildfires in San Diego County, and (d) the Surface Fuels Layer showing the vegetation fuels northwest of Los Angeles. The WIFIRE services and data sources implementing these layers are discussed in Sections 3.2-3.5. Firemap contains several interactive components to perform predictive wildfire modeling. A user first draws the initial fire perimeter on the map, and then specifies the weather conditions and simulation time using the configuration dialog shown in Figure 3 (a). The weather parameters for air temperature, wind speed and direction, and relative humidity can be adjusted manually allowing the user to investigate “what-if” scenarios. The weather can also be set based on Santa Ana conditions, current or historical observations nearest the initial fire perimeter, or the weather forecast. Firemap queries Pylaski for the latter two scenarios. When the Run button is pressed, Firemap sends the configuration parameters to Kepler WebView, which runs the fire model. After the simulation finishes, Kepler WebView sends the fire growth perimeters to Firemap, which are then visualized in a new layer added to the map as shown in Figure 3 (b). The estimated time of arrival, or ETA, shown next to the perimeters on the map denotes the fire arrival times. Additionally, the weather conditions used for the simulation, and area burned by the fire can be displayed. The input parameters and resulting fire perimeters can be saved and shared between users. Section 3.4 describes the workflows to run the fire model, and save and share the results.
3.2
Pylaski
Pylaski is a REST service providing uniform access across different data sources and types as shown in Figure 4. When a request arrives at Pylaski, the appropriate data sources are queried based on the parameters in the request. Many data sources are external to the WIFIRE architecture and accessible via a REST service. Since each data source (both local and external) has a unique set of query parameters, Pylaski translates the query parameters from the client into the query parameters for each data source. Additionally, the data returned from different sources have different schema and possibly different formats, e.g., JSON and XML. Pylaski integrates all the results from different sources into a unified GeoJSON schema that is returned to the client. 4
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Figure 2: Firemap layers: (a) Weather Stations Layer showing MesoWest stations in San Diego County during Santa Ana conditions on 26 September, 2016; (b) VIIRS Layer showing 7 days of thermal detections of the Rey Fire, north of Santa Barbara, CA, during 14-21 August, 2016; (c) Historical Fires layer showing wildfires in San Diego County during 1970-2015; and (d) Surface Fuels Layer showing the vegetation fuels for the canyons northwest of Los Angeles in 2014. Since the datasets accessible by Pylaski are very large, the REST query parameters primarily limit the data spatially, temporally, by type, or by some combination of these. Spatial queries find the measurements nearest to a specific set of latitude and longitude coordinates, or retrieve measurements taken within a bounding box. Data can also be filtered temporally by requesting the latest data or archived data within a specific time frame. Finally, the data to retrieve can be limited to a specified set of types, e.g., to run the wildfire model, we can limit the results to air temperature, wind speed and direction, and relative humidity. The following describes the data sources provided by Pylaski and how they are used in Firemap: • Weather stations: MesoWest and Synoptics Labs [13] provide observations of over 30,000 weather stations primarily located throughout North America. The Weather Stations Layer shows the locations and latest measurements from these stations as shown in Figure 2 (a). Additionally, these stations can provide the wildfire model with the current or historical weather conditions at the station nearest the initial fire perimeter. • Weather forecast: The High-Resolution Rapid Refresh (HRRR) weather forecast has a horizontal spatial resolution of 3km and temporal resolution of 15 minutes. HRRR re5
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(a)
(b)
Figure 3: Firemap predictive modeling: (a) the configuration dialog specifying the simulation time, and weather conditions; (b) predicted fire spread for the Blue Cut Fire showing the weather conditions, fire perimeters, area, and arrival times. Client
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Figure 4: Pylaski service and external data sources. quires an Environmental Data Exchange (EDEX) server to receive the feed, and the high temporal resolution enhances the accuracy of the wildfire model prediction based on the forecasted weather. • Camera images: The High Performance Wireless Research and Education Network (HPWREN) [9] and San Diego Gas and Electric (SDG&E) [28] operate tower-mounted cameras throughout San Diego County. The cameras are mostly located on mountaintops, which provide ideal viewpoints for locating wildfires in the backcountry. AlertTahoe [21] operates tower-mounted cameras around Lake Tahoe and throughout Nevada. Most of these cameras are pan-tilt-zoom (PTZ), which can be changed to focus on a wildfire. The Camera Layer shows the location and images from these cameras. • Air Quality Stations: OpenAQ [27] aggregates air quality data from over 40 countries. The Air Quality Layer shows the location and latest measurements from these stations.
3.3
GeoServer
Firemap uses GeoServer [23], an open-source server for accessing geospatial datasets, to display GIS data. GeoServer implements a number of open standards from the Open Geospatial 6
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Consortium (OGC) such as Web Feature Service (WFS), and Web Map Service (WMS), and Web Coverage Service (WCS). Firemap queries the WMS service to implement several visualization layers. WMS queries return images of the data, which GeoServer reads from a PostGIS database for vector data, and from raster files for raster data as shown in Figure 5. PostGIS Historical Fires Satellite Detections Smoke Areas
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Figure 5: GeoServer service and geospatial datasets. Satellite dections, smoke areas, and red flag warnings are automatically added to the PostGIS database every hour. The following Firemap layers are provided GeoServer: • Historical Fires: wildfire perimeters in the U.S. during 2000-2015 from GeoMAC [24] and in California during 1878-2014 from CAL FIRE [22]. This layer displays the wildfire perimeters and clicking on a perimeter displays information about the wildfire include the name, start and containment dates, and total area as shown in Figure 2 (c). • Satellite Detections: thermal imaging wildfire detections from VIIRS [17] and MODIS [14] with 375m and 1km resolution, respectively. Both satellites fly over the same area approximately twice a day. The detections are downloaded from NASA and ingested to the PostGIS database every hour. Figure 2 (b) shows seven days of VIIRS detections of the Rey Fire. • Smoke Areas: smoke plumes indicating possible wildfire locations. The smoke data is retrieved from NOAA’s Hazard Mapping System [15] and ingested to the PostGIS database every hour. • Red Flag Areas: regions currently experiencing weather conditions that “may result in extreme burning conditions” [19] such as high wind speeds and low relative humidity. The Red Flag warnings are downloaded from the National Wildfire Coordinating Group [26] and ingested to the PostGIS database every hour. • Census Blocks: number of people and housing units from the 2010 U.S. Census. The number of people in a neighborhood threatened by a wildfire is crucial when determining evacuation times. Firemap provides both a graphical layer showing census blocks and the ability to query census information for user-drawn areas. • Surface Fuels: classification of surface vegetation into thirteen fire behavior fuel models [1, 4] from LANDFIRE [11]. Surface fuels are a critical input parameter for fire modeling and Firemap provides a layer to visualize this data as shown in Figure 2 (d). • Canopy Cover: percentage of forest floor covered by trees from LANDFIRE [12]. The canopy cover is used to model crown fires, i.e., fires that spread in the top layer of tree foliage. • Camera Viewsheds: geographical areas with line-of-sight visibility surrounding the HPWREN and AlertTahoe cameras. The Camera Viewsheds Layer visualizes the regions visible and not visible by the cameras. 7
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3.4
Kepler WebView
Kepler WebView [5] integrates web technologies with the Kepler Scientific Workflow System [2]. A lightweight web server runs inside the Kepler process and provides real-time communication between clients and Kepler workflows. A client can use either REST or WebSockets to execute workflows, and data is exchanged as JSON. Kepler workflows are composed of a set of executable components, called actors, linked together to form an overall execution plan. Each actor has one or more input and output ports from which they can be connected to other actors thereby creating data dependencies. Actors may also have a set of parameters to configure their execution. Kepler workflows also contain directors, which specify the execution semantics such as the model of computation and how actors communicate with each other. A Kepler workflow has been created to run the FARSITE fire growth simulator [7]. The inputs include the weather conditions, simulation time, and initial fire perimeters. The workflow creates the FARSITE configuration files based on these parameters, and executes FARSITE. After FARSITE completes the simulation, Kepler WebView sends the resulting fire perimeters back to the client browser, which are then added as a new layer in Firemap. FARSITE writes the fire growth perimeters to an ESRI Shapefile, and Kepler WebView automatically converts this to GeoJSON when sending the results to Firemap. Fire perimeters generated by a single FARSITE execution, or fire run, may be shared between users and downloaded as KML or Shapefiles. This functionality is implemented in several Kepler workflows, which employ GIS actors to convert between geospatial data formats and projections, and SQL actors to query and update a PostGIS database. The following workflows have been implemented for Firemap: • RunFarsite: create FARSITE configuration files based on input parameters, execute FARSITE, and return the resulting fire perimeters. • ExportRun: convert a fire run’s perimeters to either KML or an ESRI Shapefile and download them on the client machine via the browser. • ShareFireRun: save the fire run’s input parameters, e.g., weather conditions, simulation length, etc., output fire perimeters, along with who executed the fire run to a PostGIS database. • ListSharedRuns: list the fire runs stored in the PostGIS database.
• GetSharedRuns: load one or more fire runs from the PostGIS database. The retrieved fire runs are added to Firemap as fire perimeter layers.
3.5
LiveWx
LiveWx provides real-time measurements from HPWREN weather sensors co-located with many of the tower-mounted cameras. These sensors report wind measurements every second, and air temperature, air pressure, and relative humidity every ten seconds. LiveWx sends new measurements to clients via a WebSocket as soon as the data appears on the HPWREN multicast network. A WebSocket is used instead of REST since there is a continual stream of measurements from the sensors. When the Real-time Weather Stations Layer is enabled, Firemap opens a WebSocket connection to LiveWx, and updates the layer in real-time as measurements are received. These high-frequency updates not only give the current observations, but also highlight the variability in weather conditions, which is lost for stations that provide measurements less frequently such as once every ten minutes or hour. 8
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Conclusions and Future Work
This paper has presented the Firemap interactive web application for predictive fire modeling and visualization, and explained how Firemap interacts with the Pylaski, GeoServer, Kepler WebView, and LiveWx data services within WIFIRE. The presented extensions to the WIFIRE end-to-end cyberinfrastructure, for the first time, enables firefighting and other emergency officials to perform near real-time data-driven predictive wildfire modeling via web-enabled visual interfaces. The Firemap tools greatly simplify visualization of both current and historical geospatial datasets related to wildfires such as weather observations, satellite detections, and surface fuels. We are working to enhance the Firemap architecture in several ways. Data assimilation demonstrated in the WIFIRE system will be integrated into Firemap to improve prediction accuracy by adjusting for limited or noisy data. We are also exploring machine learning techniques to predict model execution time to provide better feedback in Firemap and to optimize job scheduling for large long-running fire models. Additional data sources are being investigated such as drone measurements and imagery, and social network feeds relating to active wildfires. The extensible nature of the Firemap web tool and the WIFIRE cyberinfrastructure enables integration of not only new data sources and fire modeling improvements, but also machine learning on location specific characterization of environmental conditions and deep learning for fire surveillance and fuel monitoring using high-resolution imagery. As a conclusion, our interactions to date with firefighting agencies and wildfire modeling community has proven the usefulness of the developed tools both in the context of real-life fire situations and synthesizing data for wildfire modeling improvements. Although the modeling products have been endorsed as within expected boundaries by the operations fire behavior analysts we have worked with, we are currently working on a more scientific evaluation of the accuracy of the predictive models in comparison to final fire boundaries.
References [1] Frank A Albini. Estimating wildfire behavior and effects. Technical Report GTR-INT-30, Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, 1976. [2] I. Altintas, C. Berkley, E. Jaeger, M. Jones, B. Ludascher, and S. Mock. Kepler: an extensible system for design and execution of scientific workflows. In 16th International Conference on Scientific and Statistical Database Management, pages 423–424, June 2004. [3] Ilkay Altintas, Jessica Block, Raymond de Callafon, Daniel Crawl, Charles Cowart, Amarnath Gupta, Mai Nguyen, Hans-Werner Braun, J¨ urgen P. Schulze, Michael Gollner, Arnaud Trouve, and Larry Smarr. Towards an integrated cyberinfrastructure for scalable data-driven monitoring, dynamic prediction and resilience of wildfires. In Proc. of the Int. Conf. on Computational Science, ICCS 2015, pages 1633–1642, 2015. [4] Hal E Anderson. Aids to determining fuel models for estimating fire behavior. The Bark Beetles, Fuels, and Fire Bibliography, page 143, 1982. [5] Daniel Crawl, Alok Singh, and Ilkay Altintas. Kepler WebView: A Lightweight, Portable Framework for Constructing Real-time Web Interfaces of Scientific Workflows. Procedia Computer Science, 80:673 – 679, 2016. International Conference on Computational Science (ICCS). [6] M.A. Finney and P.L. Andrews. FARSITE - A Program for Fire Growth Simulation. Fire Management Notes, 59:13–15, 1999. [7] Mark A. Finney. FARSITE: Fire Area Simulator-Model. Development and Evaluation. USDA Forest Service Research Paper, RMRS-RP-4, 2004.
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