Effect of climate change on fire regimes in natural resources of northern Iran: investigation of spatiotemporal relationships using regression and data mining models

Saeedeh Eskandari (), Hooman Ravanbakhsh (), Yazdanfar Ahangaran (), Zolfaghar Rezapour () and Hamid Reza Pourghasemi ()
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Saeedeh Eskandari: Agricultural Research Education and Extension Organization (AREEO)
Hooman Ravanbakhsh: Agricultural Research Education and Extension Organization (AREEO)
Yazdanfar Ahangaran: Natural Resources and Watershed Organization of Iran
Zolfaghar Rezapour: Kohgiluyeh and Boyer Ahmad Meteorological Administration
Hamid Reza Pourghasemi: Shiraz University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 119, issue 1, No 19, 497-521

Abstract: Abstract Mazandaran province in northern Iran is one of the fire-prone areas in the country in which a wide area of its natural resources have been destroyed by fire in recent years. This research aimed to detect the spatiotemporal relationships between climatic variables and fire regimes in Mazandaran province in recent decades. The fire variables (dependent variables) were the number and area of fires. The climatic variables (independent variables) were seasonal temperature mean, seasonal maximum temperature mean, seasonal absolute maximum temperature, seasonal precipitation mean, seasonal relative humidity mean, seasonal wind speed mean, and seasonal maximum wind speed mean for 26 years (1996–2021). Pearson's correlation coefficient and regression models were used to investigate the temporal relationship between fire and climatic variables during study period. Data mining models were used to detect the spatial relationship between fire ignition and climatic parameters and to produce the fire danger maps. The fire occurrence map was obtained from Mazandaran Natural Resources and Watershed Administration and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The climatic maps were obtained by interpolation methods in GIS. The weight of climatic parameters in fire ignition was determined using MDG and MDA statistics from random forest (RF) algorithm. Then different data mining models (logistic regression, random forest, support vector machine, and SVM-RF ensemble model) and 70% of actual fires were used for modeling fire danger in R software. The area under the curve and 30% of actual fires were applied for accuracy assessment of the models. Results of temporal relationships indicated that there are significant relationships among the number of fires and seasonal absolute maximum temperature, seasonal precipitation mean, and seasonal relative humidity mean. On the other hand, a significant relationship was observed between the area of fires and seasonal temperature mean. The results of spatial relationship demonstrated that seasonal temperature mean, seasonal precipitation mean, and seasonal relative humidity mean had the greatest spatial importance in fire ignition. The results of accuracy assessment of fire danger models indicated that SVM-RF and RF models were the best models for fire danger mapping. Therefore, using the maps obtained from these models, it is possible to predict the climate-caused fires in natural ecosystems of Mazandaran province.

Keywords: Climatic variables; Fire temporal analysis; Fire spatial analysis; Linear regression; Data mining models (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06133-4

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