A Comparative Study of Genetic Algorithm-Based Ensemble Models and Knowledge-Based Models for Wildfire Susceptibility Mapping

Abdel Rahman Al-Shabeeb (), Ibraheem Hamdan, Sedigheh Meimandi Parizi, A’kif Al-Fugara, Sana’a Odat, Ismail Elkhrachy, Tongxin Hu and Saad Sh. Sammen ()
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Abdel Rahman Al-Shabeeb: Department of GIS and Remote Sensing, Faculty of Earth and Environmental Sciences, Al al-Bayt University, Mafraq 25113, Jordan
Ibraheem Hamdan: Department of Applied Earth and Environmental Sciences, Faculty of Earth and Environmental Sciences, Al al-Bayt University, Mafraq 25113, Jordan
Sedigheh Meimandi Parizi: Department of Civil Engineering, Sirjan University of Technology, Sirjan 7813733385, Iran
A’kif Al-Fugara: Department of Surveying Engineering, Faculty of Engineering, Al al-Bayt University, Mafraq 25113, Jordan
Sana’a Odat: Department of Earth and Environmental Sciences, Faculty of Science, Yarmouk University, Irbid 21163, Jordan
Ismail Elkhrachy: Civil Engineering Department, College of Engineering, Najran University, King Abdulaziz Road, Najran 66454, Saudi Arabia
Tongxin Hu: Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, College of Forestry, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China
Saad Sh. Sammen: Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 10047, Iraq

Sustainability, 2023, vol. 15, issue 21, 1-25

Abstract: Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies.

Keywords: fire susceptibility mapping; AHP; support vector machine; random subspace; ensemble models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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