The focus of our study was to create a Main Map of fire occurrence zones from historical wildland fire ignition observations at national level in Greece using a Kernel Density estimation procedure. ...Kernel density estimation, a non parametric statistical method for estimating probability densities, has been widely used for home range estimation in wildlife ecology. It has the advantage of directly producing density estimates that are not influenced by grid size and localization effects. Furthermore, it produces densities of any shape and analyzes any data distributed multi-modally or non-normally. Under this perspective, kernel density surfaces have been created to construct fire occurrence zones. Their observed distribution was statistically significantly different than the expected one that arises under complete spatial randomness. A smoothing effect is certainly observed when increasing the bandwidth size of the kernel density interpolation. Excluding the kernel size of 1000 meters, then the results do not prove any influence of kernel size or control points on the kernel density surfaces.
Effective wildfire management is an essential part of forest firefighting strategies to minimize damage to land resources and loss of human lives. Wildfire management tools often require a large ...number of computing resources at a specific time. Such computing resources are not affordable to local fire agencies because of the extreme upfront costs on hardware and software. The emerging cloud computing technology can be a cost- and result-effective alternative. The purpose of this paper is to present the development and the implementation of a state-of-the-art application running in cloud computing, composed of a wildfire risk and a wildfire spread simulation service. The two above applications are delivered within a web-based interactive platform to the fire management agencies as Software as a Service (SaaS). The wildfire risk service calculates and provides daily to the end-user maps of the hourly forecasted fire risk for the next 112 hours in high spatiotemporal resolution, based on forecasted meteorological data. In addition, actual fire risk is calculated hourly, based on meteorological conditions provided by remote automatic weather stations. Regarding the wildfire behavior simulation service, end users can simulate the fire spread by simply providing the ignition point and the projected duration of the fire, based on the HFire algorithm. The efficiency of the proposed solution is based on the flexibility to scale up or down the number of computing nodes needed for the requested processing. In this context, end users will be charged only for their consumed processing time and only during the actual wildfire confrontation period. The system utilizes both commercial and open source cloud resources. The current prototype is applied in the study area of Lesvos Island, Greece, but its flexibility enables expansion in different geographical areas.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Α web-based Geographic Information Systems (GIS) platform – named Virtual Fire – for forest fire control has been developed to easily, validly and promptly share and utilize information and tools ...among firefighting forces. This state-of-the-art system enables fire management professionals to take advantage of GIS capabilities without needing to locally install complex software components. Fire management professionals can locate fire service vehicles and other resources online and in real-time. Fire patrol aircrafts and vehicles may use tracking devices to send their coordinates directly to the platform. Cameras can augment these data by transmitting images of high-risk areas into the graphical interface of the system. Furthermore, the system provides the geographical representation of fire ignition probability and identifies high-risk areas at different local regions daily, based on a high performance computing (HPC) pilot application that runs on Windows HPC Server. Real-time data from remote automatic weather stations and weather maps based on a weather forecasting system provide vital weather data needed for fire prevention and early warning. By using these methods and a variety of fire management information and tools, the end-users are given the ability to design an operational plan to encompass the forest fire, choosing the best ways to put the fire out within the proper recourses and time.
•For fire agencies that do not have the know-how to operate their own IT systems.•Fire protection officers take advantage of GIS capabilities w/out complex training.•Authorities may design effective plans for forest fire prevention and monitoring.
In this work, we provide a framework for assessing cross-boundary wildfire exposure and a case study application in the region of Macedonia, Greece. The required spatial layers describing topography, ...fuels/ vegetation and ignition location were retrieved from open-access international and national databases, while climate data were obtained from remote automatic weather stations. We processed the spatial layers to derive the required inputs for the Minimum Travel Time fire spread algorithm. Hourly and daily weather data were used to derive the most frequent wind directions and to characterize the extreme wind speed scenarios for the season with the lowest dead and live fuel moisture contents (July and August). The study region was divided into 30 zones of similar climatic conditions and historical wildfire activity (i.e., simulation scenarios that included simulation duration, wind data, fuel moisture content and spotting probability). For model calibration, we replicated the historical large wildfire size (>50 ha) distribution of each zone by simulating thousands of potential wildfires with the derived simulation scenarios. Ignitions were allocated within burnable fuels according to an ignition probability grid developed from a historical fire occurrence database. We simulated over 300,000 fires, each independently modelled with constant weather conditions considering a randomly chosen simulation scenario. Scenario selection was based on a predefined selection probability derived by the historical wind direction frequency and fire duration of the ignition location zone. Simulations generated a layer of fire perimeters and raster estimates of annual burn probabilities and conditional flame length. Results were used to estimate community exposure by intersecting simulated fire perimeters with community polygons. The number of exposed structures was assigned to each simulated fire ignition, estimating its influence on each community (one ignition to many communities). The post-processing of these ignitions generated community firesheds, which delineate the area around communities where large fires are likely to be transmitted to the burnable and populated community area polygons. We found that on 9,250 km2, or the 27% of the study area, potential ignitions can grow large enough to reach communities and cause structure exposure. The proposed framework can guide future efforts aimed at quantifying community exposure to large-scale wildfires and guide investments to prioritize fuel management activities to reduce fire risk.
As the demand for geospatial data increases, the lack of efficient ways to find suitable information becomes critical. In this paper, a new methodology for knowledge discovery in geographic portals ...is presented. Based on the Semantic Web, our approach exploits the Resource Description Framework (RDF) in order to describe the geoportal's information with ontology-based metadata. When users traverse from page to page in the portal, they take advantage of the metadata infrastructure to navigate easily through data of interest. New metadata descriptions are published in the geoportal according to the RDF schemas.
This study investigated the wind characteristics of the island of Lesvos, Greece, with the objective of providing the necessary data for identifying the wind power production capabilities of the ...island. Weather patterns were examined using weather data from four Remote Automatic Weather Stations. Specific tools were used to produce the necessary windroses, Weibull curves and charts that helped to understand the prevailing wind characteristics. By using the tools of Geographic Information Systems (GIS) and the Wind Atlas Analysis and Application Program (WAsP) as the basic calculation platform, a wind map was produced portraying the wind speeds that prevail at a height of 10
m above ground level. The results of the analysis were tested and evaluated with measurements from 15 wind turbine sites by creating six alternative scenarios. The optimum scenario was used to investigate the installation of a small wind farm with five wind turbines, of 3 MW total capacity.
•This paper quantifies the effect of fuzzy clustering in the design process of a typical RBF network.•It is analytically shown that the fuzzy clustering acts to minimize an upper bound of the ...network’s square error.•The PSO algorithm is used to minimize the upper bound and to provide an estimation of the network’s parameters.•Finally, the widths and connection weights are further tuned using a steepest descent approach.
This paper proposes a novel training algorithm for radial basis function neural networks based on fuzzy clustering and particle swarm optimization. So far, fuzzy clustering has proven to be a very efficient tool in designing such kind of networks. The motivation of the current work is to quantify the exact effect of fuzzy cluster analysis on the network’s performance and use it in order to substantially improve this performance. There are two key theoretical findings resulting from the present work. First, it is analytically proved that when the standard fuzzy c-means algorithm is used to generate the input space fuzzy partition, the main effect this partition imposes to the network’s square error (i.e. performance index) can be written down in terms of a distortion function that measures the ability of the partition to recreate the original data. Second, using the aforementioned distortion function, an upper bound of the network’s square error can be constructed. Then, the particle swarm optimization (PSO) is put in place to minimize the above upper bound and determine the network’s parameters. To further improve the accuracy, the basis function widths and the connection weights are fine-tuned by employing a steepest descent approach. The main experimental findings are: (a) the implementation of the PSO obtains a significant reduction of the square error while exhibiting a smooth dynamic behavior, (b) although the steepest descent further decreases the error it finally obtains smaller reduction rates, meaning that the strongest impact on the error reduction is provided by the PSO, and (c) the improved performance of the proposed network is demonstrated through an extensive comparison with other related methods using a 10-fold cross-validation analysis.
Novel technological advances in mobile devices and applications can be exploited in wildfire confrontation, enabling end-users to easily conduct several everyday tasks, such as access to data and ...information, sharing of intelligence and coordination of personnel and vehicles. This work describes an innovative mobile application for wildfire information management that operates on Windows Phone devices and acts as a complementary tool to the web-based version of the AEGIS platform for wildfire prevention and management. Several tasks can be accomplished from the AEGIS App, such as routing, spatial search for closest facilities and firefighting support infrastructures, access to weather data and visualization of fire management data (water sources, gas refill stations, evacuation sites etc.). An innovative feature of AEGIS App is the support of these tasks by a digital assistant for artificial intelligence named Cortana (developed by Microsoft for Windows Phone devices), that allows information utilization through voice commands. The application is to be used by firefighting personnel in Greece and is potentially expected to contribute towards a more sophisticated transferring of information and knowledge between wildfire confrontation operation centers and firefighting units in the field.
Social media is rapidly emerging as a potential resource of information capable to support natural disasters management. Despite the growing research interest focused on using social media during ...natural disasters, many challenges may arise on how to handle the ‘big data’ problem: huge amounts of geo-social data are available, in different formats and varying quality that must be processed quickly. This article presents a state-of-the-art approach towards the enhancement of decision support tools for natural disaster management with information from the Twitter social network. The novelty of the approach lies in the integration of Geographic Information Systems (GIS) modeling outputs with real-time information from Twitter. A first prototype has been implemented that integrates geo-referenced Twitter messages into a Web GIS for wildfire risk management and real-time earthquake monitoring. Following a highly scalable architecture that relies on big data components, the proposed methodology can be applied in different geographical areas, different types of social media and a variety of natural disasters. The article aims at highlighting the role of social big data, towards a more sophisticated transfer of knowledge among civil protection agencies, emergency response crews and affected population.
This study proposes and evaluates a relatively new concept for fire occurrence zoning based on documented historical fire records. The proposed method creates continuous kernel density surfaces based ...on wildland fire ignition observations. Kernels have the advantage of directly producing density estimates that are not influenced by grid size or localization effects. Within this scheme, kernel density surfaces have been created and reclassified to construct fire occurrence zones at local to global scales in the Mediterranean Basin. Specifically, fire occurrence zones were created for the European scale (European Mediterranean Basin), national scale (Greece), regional scale (Peloponnese, Greece) and local scale (Chalkidiki, Greece). To evaluate fire occurrence zones, we compared the observed with the expected distribution of the number of fires within these zones using a Monte Carlo randomization test, finding that these numbers were statistically different in all cases. The deviations observed from the expected distributions towards the high occurrence zone indicated their successful assessment and value. In this paper, we further discuss their potential role and use for multi-scale fire management and policy in a European context.
PDF
