Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran

Saeedeh Eskandari (), Mahdis Amiri (), Nitheshnirmal Sãdhasivam () and Hamid Reza Pourghasemi ()
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Saeedeh Eskandari: Research Institute of Forests and Rangelands (RIFR), Agricultural Research Education and Extension Organization (AREEO)
Mahdis Amiri: Gorgan University of Agricultural Sciences and Natural Resources
Nitheshnirmal Sãdhasivam: Bharathidasan University
Hamid Reza Pourghasemi: Shiraz University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 104, issue 1, No 13, 305-327

Abstract: Abstract The forest fire hazard mapping using the accurate models in the fire-prone areas has particular importance to predict the future fire occurrence and allocate the resources for preventing the fire ignition. This research aimed to compare the accuracy of some individual models including boosted regression tree (BRT), classification and regression trees (CART), functional discriminant analysis (FDA), generalized linear model (GLM), mixture discriminant analysis (MDA), random forest (RF) and two new hybrid models including FDA-GLM-MDA and RF-CART-BRT for predicting the fire hazard in a fire-prone area in the northeast Iran, Golestan Province. For this purpose, a comprehensive dataset from ten effective parameters including digital elevation model (DEM), slope angle (SA), plan curvature (PC), topographic wetness index (TWI), annual rainfall mean (ARM), annual temperature mean (ATM), wind effect (WE), distance to urban areas (DTU), distance to streams (DTS) and distance to roads (DTR) was created in GIS. Furthermore, 3705 historical fire locations in the Golestan Province from 2002 to 2017 were obtained from MODIS fire product dataset. Then, the variable importance was assessed using the XGBoost machine learning (ML) technique. Finally, the individual and hybrid models were evaluated using the ROC-AUC method. The results showed that the DTU was the most important factor in modeling and mapping the fire hazard in the Golestan Province. Also, the results demonstrated that the individual random forest (RF) (AUC = 0.855) and hybrid RF-CART-BRT algorithms (AUC = 0.854) were the most accurate predictive models for mapping the fire hazard in the Golestan Province, respectively. Considering the high significance of DTU in fire occurrence in this study, the area of fire hazard classes in fourteen different counties of the Golestan Province was calculated using the most accurate model (RF model). The final results indicated that Minudasht County had the most area (35.55%) of fire hazard in the very high fire hazard class. The results of this study are very useful for local forest managers to control the future fires using the best model in the natural areas of the Golestan Province, especially in Minudasht County. The protective management of the natural areas of the Golestan Province would be performed based on the fire hazard maps produced by RF and RF-CART-BRT algorithms. We recommend applying these models for fire danger mapping in fire-prone areas around the world which have semi-arid conditions. More comparative assessment of individual and ensemble models for fire danger mapping in semi-arid areas around the world could provide a baseline for monitoring fire danger in similar conditions.

Keywords: Fire hazard mapping; Individual and hybrid modeling; Effective factors; XGBoost; Machine learning; Distance to urban areas (DTU) (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-020-04169-4

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