Modeling the Spatial Variability of Forest Fire Susceptibility Using Geographical Information Systems and the Analytical Hierarchy Process

Gigović, Ljubomir; Pamučar, Dragan; Drobnjak, Siniša; Pourghasemi, Hamid Reza

doi:10.1016/b978-0-12-815226-3.00015-6

Cited by 32 publications

(23 citation statements)

References 63 publications

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“…According to the review of forest re in the Indian context done by Joseph et al (2009), topographical features like altitude, aspect, slope, Topographic Wetness Index (TWI) have been used in forest re prediction (Arpaci et al, 2014;Estes et al, 2017;Flannigan & Harrington, 1988;Guo et al, 2016;Jaafari et al, 2018;T. Kim et al, 2015;Ljubomir et al, 2019;Sachdeva et al, 2018;Sharma et al, 2014;Tien Bui et al, 2019;Yathish et al, 2019). These features are partly accountable for the type and distribution of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya

Banerjee

2021

Scandinavian Journal of Forest Research

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The recent episodes of forest res in Brazil and Australia of 2019 are tragic reminders of the hazards of forest re. Globally incidents of forest re events are on the rise due to human encroachment into the wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest re during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest re prediction map has been prepared using a forest re inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of forest res. Model validation criteria like ROC curve, correlation coe cient, and Cohen's Kappa show a good predictive ability (AUC = 0.95, COR = 0.81, κ = 0.78). The outcomes of this study in the form of a forest re prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

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Section: Introductionmentioning

confidence: 99%

Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya

Banerjee

2021

Scandinavian Journal of Forest Research

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“…Model validation is a fundamental step in any natural hazards study [115]. The well-known area under curve (AUC) technique has been used in many natural hazard susceptibility mapping studies [116][117][118], producing the prediction and success rates by means of a comprehensive quantitative method [119]. The validation was achieved by comparing the wildfire inventory data and derived probability maps.…”

Section: Ensemble Modelingmentioning

confidence: 99%

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

et al. 2021

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The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

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“…Fuzzy Logic principles in set theory constitute Fuzzy Set eory [46] that allows partial membership, i.e., contains the elements with variable membership in the set. [47].…”

Section: Fuzzy Set Eorymentioning

confidence: 99%

An Intelligent Framework for Analyzing the Feasible Modes of Transportation in Metropolitan Cities: A Hybrid Multicriteria Approach

Srivastava

Zhang

Eachempati

et al. 2021

Journal of Advanced Transportation

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The paper aims to build a hybrid personalized multicriteria model in the Indian transportation industry to identify the most feasible transport mode suitable for commuters’ customized preferences. A hybrid multicriterion model, i.e., Fuzzy Analytical Hierarchy Process (AHP), was used to compute the criteria weights, which were subsequently analyzed by three approaches, namely, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy TOPSIS, Evaluation Based on Distance from Average Solution (EDA), and Interpretive Ranking Process (IRP). The case of an Indian metropolitan city, Hyderabad, is taken to illustrate the proposed approach. The paper highlights the following transport modes: metropolitan train (unconventional mode) and conventional modes such as the car, public bus transport, and bikes for Hyderabad. Furthermore, sensitivity analysis is performed to identify the consistency in ranking with variation in weights, and the Ensemble Ranking and transportation experts validate the rankings.

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Modeling the Spatial Variability of Forest Fire Susceptibility Using Geographical Information Systems and the Analytical Hierarchy Process

Cited by 32 publications

References 63 publications

Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya

Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

An Intelligent Framework for Analyzing the Feasible Modes of Transportation in Metropolitan Cities: A Hybrid Multicriteria Approach

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