Applying tools of geoinformation modeling for fire hazard data mining

Розглянуто методологічні підходи до використання індексних показників стану вегетації на основі даних дистанційного зондування програми Copernicus в якості підґрунтя для формування індексу пожежної небезпеки на територію України. Розроблено модель обробки даних глобальних земельних сервісів Copernicus з метою отримання значень індексу пожежної небезпеки. Отримані результати моделювання було верифіковано за допомогою даних про природні пожежі з використанням методів розрахунку географічно-зваженої регресії та кореляції між геопростовими даними.The methodological approaches to the use of index indicators of vegetation based on Copernicus program remote sensing data as a basis for forming anfire hazard index on the territory of Ukraine were considered. The purpose of this study is to develop indicative model based on the basic vegetation parameters of Copernicus program and its verification using available data about the monitoring of natural fires. As input parameters were chosen Normalized Difference Vegetation Index (NDVI), which is an indicator of the greenness of the biomes;Dry Matter Productivity (DMP), represents the overall growth rate or dry biomass increase of the vegetation, expressed in kilograms of dry matter per hectare per day; the Soil Water Index quantifies the moisture condition at various depths in the soil. Based on methods of combining data of different nature and mechanisms of normalization of geospatial data was obtained integral fire hazard index. The model of data processing of global land services Copernicus was developed for obtain fire hazard index values, which is implemented in the environment of ArcGIS 10.3. The results were verified by simulation using data on natural fires product Burnt Area using calculation methods geographically weighted regression and correlation of geospatial data. This was found closeness link between raster model fire hazard index and raster model of the density distribution of fires that have already appeared, which was calculated using optimization methods by establishing a standard distance. The close link between high values of the fire hazardindex and high density of recorded fires was established. In the area of low values of indicators tightness of links is significantly reduced. This suggests the possibility of using ranges of high values of the fire hazard index for predicting natural fires with high probability and extends the scope of products processing usingof program Copernicus

Інтелектуальний аналіз небезпеки виникнення природних пожеж на основі геоінформаційних технологій

Natural fires are one of the biggest threats to the economy and population in Ukraine. Due to the limited materials and equipment it is necessary to redistributing them according to the level of danger. Therefore problems of fire risk assessment for the management of fire prevention at the national and regional level remain a problem of management in terms of situational uncertainty.With a view to its elimination it was proposed based on mathematical tools to conduct data mining classification in Ukraine on grounds of belonging to the fire dangerous area. As the signs were chosen factors influencing the occurrence of natural fires: relief features, climatic characteristics and land cover. As a method was chosen classification features based on the decision tree algorithm C4.5, which allows to use existing classification criteria for classification of the surface cells to a particular class of fire risk.Further use of the typical tools of GIS based on ArcGis platform allow to obtain the total value of the risk of fire danger based on raster algebra and summarize them for each administrative unit. The implementation of this zoning for Ukraine can detect the most dangerous areas in terms of natural fires and pursue advance training of human resources and preparation of material resources to prevent major damage from fires.Рассмотрено методику построения модели оценки опасности возникновения природных пожаров на основе использования интеллектуального инструментария ГИС-анализа и алгоритма классификации С4.5. Выделено основные группы факторов, которые влияют на возникновения пожаров, реализовано методику проведения ГИС-анализа для Украины, которая содержит 5 этапов. Проведен анализ полученных результатов с целью предоставления рекомендаций для органов управления.Розглянуто методику побудови моделі оцінки небезпеки виникнення природних пожеж на основі використання інтелектуального інструментарію ГІС-аналізу та алгоритму класифікації С4.5. Виділено основні групи факторів, які впливають на виникнення пожеж, реалізовано методику ГІС-аналізу для України, яка містить 5 етапів. Проведено аналіз отриманих результатів з метою надання рекомендацій для органів управління

Environmental Influences on Large Daily Wildfire Growth in California

Wildfires have become a major environmental, social, and economic problem in California. The consequences can be especially detrimental when they exhibit behavior like very large daily growth (an individual fire burning \u3e10,000 acres over a 24-hour period). Environmental conditions influencing the risk of large daily growth include weather variables such as temperature, wind, relative humidity, and precipitation; fuel variables such as type, loading, availability, and moisture content; as well as topographic variables such as slope, aspect, elevation, and shape. However, there remains great uncertainty in the importance of these variables relative to each other and the existence of any threshold values in these variables. Our study applied random forest modeling using multivariate and high spatiotemporal data for 16,013 wildfire days in California from 2003 to 2020 to determine feature importance for the task of predicting whether a fire would burn \u3e10,000 acres over a 24-hour period. Shapely Additive Explanations indicate that 100-hour dead fuel moisture, maximum daily air temperature, and soil moisture provide the highest predictive power for large daily growth. Additionally, our study identifies thresholds where the probability of large daily growth significantly increases. These thresholds include a 100-hour dead fuel moisture value of \u3c10%, a maximum air temperature of \u3e75 F, and a 0-10 cm soil moisture of \u3c12%. Finally, we establish the number of days per year that these thresholds are being crossed has increased substantially over the last four decades

Розробка класифікації факторів пожежної небезпеки з використанням дерева рішень

Today in Ukraine there is an increased level of natural and technogenic threats and fire hazards. Therefore, an important task in identifying and assessing risks and threats is to determine fixed and variable factors that affect the potential for fires and to classify them by the available features. To solve the problem of classifying numerous factors of fires, we suggest using the method of building decision trees, which is a method of presenting rules in a hierarchical consistent structure where each object corresponds to a single node through which the decision is made. The use of the C4.5 algorithm helps build a branched decision tree and classify factors of fire danger. Three main classes of permanent environmental factors have been distinguished, which include land cover, topography, and climatic resources; the variable factors are the indices NDVI, DMP, and SWI. They, in turn, are divided into subclasses.The calculated weights can be used for simulating a fire hazard. The obtained values range from 0 to 1, where a value of 0 prevents natural fires (e. g., water surfaces), but values close to 1 indicate a high hazard potential of natural fires.The decision trees, obtained in the process of classification, are important for planning measures to prevent natural fires. They can also be used for zoning in terms of fire hazards in spatial modeling of fires, mathematical modeling of their effects, as well as in further monitoring and prediction of natural fires.Рассмотрены подходы к классификации факторов пожарной опасности с использованием метода дерева решений. Проанализированы наиболее распространённые алгоритмы классификации. С помощью алгоритма C4.5 классифицированы основные и переменные факторы, влияющие на возникновение природных пожаров и им присвоены весовые коэффициенты. Предложены направления дальнейшего использования полученных результатовРозглянуто підходи до класифікації факторів пожежної небезпеки з використанням методу дерева рішень. Проаналізовано найбільш поширенні алгоритми класифікації. За допомогою алгоритму C4.5 класифіковано основні та змінні фактори, що впливають на виникнення природних пожеж та присвоєно їм вагові коефіцієнти. Запропоновано напрями подальшого використання отриманих результаті

Watershed road network analysis with an emphasis on fire fighting management

The aim of this study is fire hazard zoning the Chehel-Chay watershed and analysis of road network in order to fire-fighting management. Using effective factors on fire occurrence, the fire hazard map of the study area produced by support vector machine algorithm and then was divided into four hazard classes. The road length and type were investigated in the each fire hazard classes. The results showed that most of occurred fires are located in the close distances of roads and forest areas. The results showed that road types and land cover are important in fire occurrences and suppression. In high dangerous zone, the roads pass through forestlands, but in low dangerous zone, the roads are passing from farmlands. The roads do not cover the half of area and do not pass at two third of high hazard class zones. Therefore, appreciate road network planning is necessary according to fire-fighting management. 

Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing

Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics

林野火災における大気・海洋変動指標と水文気象要因の定量的分析

Tohoku University博士（工学）thesi