Comparison of the effectiveness of machine learning methods in modeling fire-prone areas (Ilam Province, Darehshahr City)

Subject Areas : Natural resources and environmental management

maryam mohammadian ¹ , Maryam Morovati ² , Reza Omidipour ³

1 - MSc. Student of assessment and land use planning, Faculty of Agriculture & Natural Resources, Ardakan University, Iran
2 - Associate Professor, Department of Environmental Sciences & Engineering, Faculty of Agriculture & Natural Resources,Ardakan University, P.O.Box184,Ardakan, Iran
3 - Assisstant Prof., Department of Range and Watershed Management, Faculty of Agriculture, Ilam University, Iran

Received: 2022-10-30 Accepted : 2022-12-21 Published : 2024-06-26

Keywords: fire modeling, Random forest, ecosystem management, Support vector machine, Ilam province,

Abstract :

Fire is one of the most important natural hazards that has a great impact on the structure and dynamics of natural ecosystems. Due to Iran's location in the arid and semi-arid belt of the world, a large number of human-made and natural fires occur in different regions of the country every year. Therefore, determining sensitive areas to fire occurrence plays an important role in fire management in natural resources. To do so, the current study aims to identify fire-prone areas in Dere Shahr city in Ilam province using two machine learning of random forest (RF) and support vector machine (SVM) and 2024 fire occurrence points. Environmental factors were prepared in categories including topographical factors (altitude, slope direction, slope anlgle), climatic factors (rainfall, relative humidity, wind, temperature), biological factors (vegetation and soil moisture) and man-made factors (distance from residential areas, distance from road, distance from agricultural land, distance from river). The model’s accuracy was evaluated using the area under the curve (AUC) in the ROC curve and cross-validation statistics. Examining the AUC index showed that both models had good accuracy, although the RF model (AUC = 0.97) had higher accuracy than the support vector machine model (AUC = 0.86). According to the results of RF model, about 60% are in the low-risk class and about 20% are in the high fire risk class. Investigating the contribution of the factors affecting the occurrence of fire showed that man-made factors (distance from residential areas) and climatic factors (temperature) played a more important role in areas with a history of fire. Therefore, increasing public culture and reducing dangerous behaviors in nature can reduce the occurrence of fire in this area and contribute greatly to the protection of the environment and preservation of natural resources.