Fire risk evaluation using multicriteria analysis—a case study

Abstract

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential "hotspots" of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing "where and when" forest fires will most likely occur.

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

“…However, the complexity of the required calculations and more analysis on digital maps complicate the use of this method. On the other hand, the high reliability and accuracy of the fuzzy AHP method demonstrated by previous studies is one of the benefits of this method (Vadrevu et al 2009;Zarekar et al 2013;.…”

“…This modelling method uses expert ideas to express the importance and priority of each of the effective factors that contribute to forest fire. In this method, the fuzzy sets enter into the modelling process to express uncertainty, and to get more accurate results than the AHP method (Vadrevu et al 2009). After obtaining the weights of all effective factors in fire occurrence, all digital maps of effective factors should be adjusted to fuzzy format based on fuzzy membership functions in GIS.…”

“…However, fire risk modelling based on the spatial correlation method has been used in this research for the first time. Therefore, the reason for the use of the fuzzy AHP method is its accuracy based on previous research (Vadrevu et al 2009;Zarekar et al 2013;, the reason for the use of the spatial correlation method is its novelty, and the reason for the use of the Dong model is the commonality of its use in fire risk assessment researches (Dong et al 2005;Erten et al 2005;Eskandari et al 2013a). The modelling of forest fire risk based on these methods (models) has been described in detail in the sections below.…”

“…Fire occurrence, frequency, and intensity are primarily dependent on climate through weather conditions, which influence fuel moisture and likelihood of ignition (Vadrevu et al 2009). In addition, the role of temperature in fire occurrence in Neka and Behshahr forests (Northern Iran) has already been confirmed in other studies (Eskandari 2015).…”

“…For example, some studies have used Dong models to predict fire high-risk areas in the forests (Dong et al 2005;Erten et al 2005;Eskandari et al 2013a), whereas other researchers have applied analytic hierarchy process (AHP) or fuzzy sets to model forest fire risk (Chuvieco & Congalton 1989;Vadrevu et al 2009;Sowmya & Somashekar 2010;Mahdavi et al 2012;Zarekar et al 2013;Atesoglu, 2014;. Logistic regression has been used to model fire ignition probability (Vasconcelo et al 2001;Rollins et al 2004;Martinez et al 2009;Jurdao et al 2012;Sitanggang et al 2013;Eskandari & Chuvieco 2015), and the artificial neural network has been used to predict fire regimes (Alonso-Betanzos et al 2002;Vakalis et al 2004, Vasilakos et al 2009Satir et al 2016).…”