A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran

Abstract

Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight of evidence (WOE) models, with an emphasis placed on climatic variables. Firstly, 284 fire incidents in the region were randomly divided into two groups, including the training group (70%, 199 points) and the validation group (30%, 85 points). Given the previous studies and conditions of the region, the variables of slope percentage, slope direction, altitude, distance from rivers, distance from roads, distance from settlements, land use, slope curvature, rainfall, and maximum annual temperature were considered for zoning fire risk. Then, forest fire risk maps were prepared using the EBF and WOE models. The performance of each model was examined using the Relative Operating Characteristic (ROC) curve. The results showed that WOE and EBF are effective tools for mapping forest fire risks in the study area. However, the WOE model shows a slightly higher Area Under the Curve value (0.896) compared to that of the EBF model (0.886), indicating a slightly better performance. The results of this study can provide valuable information for preventing forest fires in the study area.

NBR Index-Based Fire Detection Using Sentinel-2 Images and GIS: A Case Study in Mosul Park, Iraq

“…Recently, severe forest fires have occurred due to climatic influences, including dry periods [15]. Forest areas close to road networks and settlements are more exposed to fires as the possibility of manmade or accidental fires increases [11].…”

“…Where exposure to particulate matter [18] from wildfire smoke is associated with negative health effects [19] and may cause death [17]. Many factors are taken into consideration to detect the forest-fire ISSN: 1686-6576 (Printed) | ISSN 2673-0014 (Online) | © Geoinformatics International risk potentially such as; temperature, wind speed, distance from roads, rainfall, slope, altitude, land use, etc [15]. Many studies showed that topography, vegetation, fire history, and climatic factors are significant variables in forest-fire susceptibility modeling [10].…”

Use of Gis Technologies for Zoning Urban Areas Taking Into Account Engineering-Geological Conditions

“…To date, geospatial data based on geographic information systems (GIS) are widely used worldwide for various purposes. For example, GIS is used for analyzing the probability of landslides and assessing the level of risk in complex landscapes [2,3], developing flood susceptibility maps [4], and creating information maps to prevent fires in forested areas under study [5,6]. They also enable the creation of interactive maps displaying the spatial distribution of various variables, such as seismic characteristics [7,8] and soil susceptibility to liquefaction [9].…”