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

• Published in: Natural hazards (Dordrecht) 2020-10, Vol.104 (1), p.305-327
• Main Authors: Eskandari, Saeedeh, Amiri, Mahdis, Sãdhasivam, Nitheshnirmal, Pourghasemi, Hamid Reza
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Annual rainfall; Annual temperatures; Arid regions; Civil Engineering; Datasets; Digital Elevation Models; Discriminant analysis; Distance; Earth and Environmental Science; Earth Sciences; Environmental Management; Fire danger; Fire hazards; Forest fires; Forest management; Forests; Generalized linear models; Geographical information systems; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Learning algorithms; Machine learning; Mapping; Model accuracy; Modelling; Natural Hazards; Original Paper; Prediction models; Rain; Regression analysis; Semi arid areas; Statistical models; Temperature effects; Urban areas; Wetness index; Wind effects
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-020-04169-4

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 wh