Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables

Abstract

Forests fires in northern Iran have always been common, but the number of forest fires has been growing over the last decade. It is believed, but not proven, that this growth can be attributed to the increasing temperatures and droughts. In general, the vulnerability to forest fire depends on infrastructural and social factors whereby the latter determine where and to what extent people and their properties are affected. In this paper, a forest fire susceptibility index and a social/infrastructural vulnerability index were developed using a machine learning (ML) method and a geographic information system multi-criteria decision making (GIS-MCDM), respectively. First, a forest fire inventory database was created from an extensive field survey and the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product for 2012 to 2017. A forest fire susceptibility map was generated using 16 environmental variables and a k-fold cross-validation (CV) approach. The infrastructural vulnerability index was derived with emphasis on different types of construction and land use, such as residential, industrial, and recreation areas. This dataset also incorporated social vulnerability indicators, e.g., population, age, gender, and family information. Then, GIS-MCDM was used to assess risk areas considering the forest fire susceptibility and the social/infrastructural vulnerability maps. As a result, most high fire susceptibility areas exhibit minor social/infrastructural vulnerability. The resulting forest fire risk map reveals that 729.61 ha, which is almost 1.14% of the study areas, is categorized in the high forest fire risk class. The methodology is transferable to other regions by localisation of the input data and the social indicators and contributes to forest fire mitigation and prevention planning.

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

“…The ML approaches fare better than statistical approaches, as shown in previous studies [64]. Moreover, compared to the other wildfire susceptibility studies in the same area and neighboring forestry areas [4,6,9,65], we used and compared many more of the available and commonly used ML approaches in order to show their capabilities with respect to wildfire susceptibility mapping.…”

“…Amol County is part of the Mazandaran province in Iran, with a total area of 4374 km 2 . The total population is around 343, 747 [6]. The study area is located on the southern coast of the Caspian Sea.…”

“…Fires are a devastating ecological feature that have several adverse consequences in diverse ways, including with respect to life, health, the economy, and demolition of natural habitats [6]. In recent years, global warming and industrialization coupled with human interference have considerably increased the occurrence and severity of forest fires in many parts of the world.…”

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

“…These often involve the implementation of physically-based models integrated into a Geographic Information System (GIS), relying on expert knowledge or including statistical analyses and modeling to assess the importance of the predisposing factors [15][16][17][18][19][20][21][22][23][24]. Lately, the comparison of deterministic physically-/statistically-based and stochastic approaches highlights the benefit of using data driven methods [25][26][27][28][29][30][31][32][33] that are able to extract knowledge directly from data. Machine learning has proven to be a mature and reliable tool in quantitative wildfire-related studies, as for the estimation of standing crop and fuel moisture content, often outperforming standard statistical approaches [34][35][36].…”

Earthquake Vulnerability Mapping Using Different Hybrid Models

“…Many studies have also used only a limited number of criteria and have carried out a one-dimensional seismic vulnerability assessment, including: [17], which assessed the economic damages of highway bridges in Campania, Italy and has used statistical methods of updating ground motion prediction and fragility (their results show that the structural dependence of land movement is an important factor in the economic damage caused by earthquakes); [18], which used an expert system containing specialized knowledge for masonry structures in assessing the seismic vulnerability of old buildings in Sri Lanka; [19], which performed a seismic vulnerability assessment by focusing on one of the Romanian cities subject to earthquake, using multi-criteria analysis and a number of physical and social criteria, the results of which identified 385 earthquake-prone structures, as well as the decision-making process to reduce the damage. Other methods and models have been used to map natural hazard vulnerability in some current studies, including certainty factors (CF) [20], ANN [21,22], logistic regression (LR) [23], support vector machine (SVM) [24][25][26], convolutional neural network (CNN) [27], ordered weight averaging (OWA) [4], fuzzy quantifier algorithm [28], adaptive neuron-fuzzy inference system (ANFIS) [29,30], and different multiple criteria decision analysis (MCDA) models [31] such as the analytic hierarchy process (AHP) [32][33][34] and the analytical network process (ANP) [35,36]. Several models and techniques have also been integrated and combined to produce more efficient hybrid models [37][38][39].…”