Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran

Abstract

This study was done to evaluate the efficiency of three methods to predict the high-risk areas for fire in District Three of Neka Zalemroud forests located in Mazandaran Province, Iran. The fuzzy analytic hierarchy process (fuzzy AHP) and the spatial correlation method were used to model fire risk in the study area. The Dong model was used to provide the fire risk map. Following the construction of fire risk maps using three methods, the map of actual fires was overlaid to validate the used methods. Then the area of the high-risk and very high-risk classes of each fire risk map within the perimeters of actual fires was calculated. Results showed that the high-risk areas in the fire risk map prepared by the fuzzy AHP and spatial correlation methods closely follow the actual fires. On the other hand, the high-risk areas in the fire risk map prepared by the Dong model showed only a moderate agreement with actual fire areas. The final results showed that the fuzzy AHP model (accuracy 0.8) and the spatial correlation model (accuracy 0.92) have the strongest ability to predict the fire high-risk areas in Hyrcanian forests of Iran, relative to the Dong model (accuracy 0.51).

GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

“…Fuzzy AHP is a technique of incorporating vagueness or fuzziness of human thoughts in decision making [ 16 ]. In the fuzzy AHP model, a combination of AHP and fuzzy sets is used to weight the contributing factors in forest fire occurrence [ 10 ] and to model forest fire susceptibility. This modelling method uses expert ideas to express the importance and priority of each factor that contributes to forest fire occurrence.…”

“…One of the key processes of MCDA is its ability to combine the values of multiple criteria with a single evaluation score for each alternative decision [ 9 ]. Thus, the integration of MDCA methods in the spatial domain provides a proper framework for fire risk assessment [ 10 ].…”

“…In order to identify the regions susceptible to fire and determine forest fire risk, various studies have been carried out worldwide. Among many methods used for modelling of forest fire susceptibility are index-based method [ 11 , 12 ], fuzzy analytic hierarchy process (AHP) [ 9 , 10 , 13 , 14 ], and fuzzy analytic network process (ANP) [ 15 ]. Since conventional AHP technique might not be adequate to provide effective decision in forest fire susceptibility mapping, fuzzy AHP technique has been used to address this issue by enabling decision makers to define approximate preferences through fuzzy numbers.…”

Fire Susceptibility Mapping in the Northeast Forests and Rangelands of Iran using New and Ensemble Data Mining Models

“…Previous studies have shown that annual mean temperature has a strong relationship with the number of res in Golestan Province (Eskandari 2015). Furthermore, the proximity to roads has also been identi ed as important factor in re susceptibility potential (Martinez et al 2009; Narayanaraj and Wimberly 2011; Rodrigues et al 2016;Eskandari and Miesel 2017;Ricotta et al 2018;Eskandari et al 2020). Wind effect has also been identi ed as an important effective factor in re occurrence (Tymstra et al 2007;Jolly et al 2015;.…”

Wildfire Severity Zoning Through Google Earth Engine and Fire Risk Assessment: Application of Data Mining and Fuzzy Multi-Criteria Evaluation in Zagros Forests, Iran

“…In the present study, there is an accurate method of extracting the burnt area where the wild re severity was estimated, and by applying the normalized difference vegetation index of Landsat satellite imagery, before and after, the wild re is discussed in Google Earth Engine platform (Parks et al, 2018). Using MCE fuzzy (Eskandari and Miesel, 2017;Kahraman et al, 2014) and LR (Pourtaghi et al, 2016;Were et al, 2015;Satir et al, 2016), wild re and risk zoning is done (Guo et al, 2016). Multi-Objective Land Allocation (MOLA) (Canova, 2006) was used to identify high-risk areas, investigate the extent of plant species degradation, and provide management strategies to combat and prevent wild re.…”