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Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms

Author

Listed:
  • Ali Nouh Mabdeh

    (Department of Earth Sciences and Environment, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan)

  • A’kif Al-Fugara

    (Department of Surveying Engineering, Faculty of Engineering, Al Al-Bayt University, Mafraq 25113, Jordan)

  • Khaled Mohamed Khedher

    (Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
    Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, Nabeul 8000, Tunisia)

  • Muhammed Mabdeh

    (Department of Earth Sciences and Environment, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan)

  • Abdel Rahman Al-Shabeeb

    (Department of Earth Sciences and Environment, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan)

  • Rida Al-Adamat

    (Department of Earth Sciences and Environment, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan)

Abstract

Support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) are two well-known and powerful artificial intelligence techniques which have been frequently used for hazard mapping. So far, a plethora of hybrid models have been developed using a combination of either the SVR or ANFIS and evolutionary algorithms, but there are only a handful of studies that compare the performance of these models when integrated with evolutionary algorithms, especially in forest fire susceptibility mapping (FFSM). The aim of this study was to compare performance of ANFIS-, and SVR-based evolutionary algorithms, namely, the genetic algorithm (GA) and the shuffled frog-leaping algorithm (SFLA) in FFSM in Ajloun Governorate in Jordan. Accordingly, four hybrid models, SVR-GA, SVR-SFLA, ANFIS-GA, and ANFIS-SFLA, were developed and compared. One hundred and one forest fire locations were used in this study to assess and model susceptibility of forests to fires. The forest fire inventory data were divided into a training data subset (70%) and a testing data subset (30%). Fourteen factors affecting incidence of forest fires were employed as conditioning factors. The area under the receiver operating characteristic (AUROC) curve was used to assess performance of the models in the validation phase. The results revealed that the SVR-based hybrid algorithms had better AUROC values than the ANFIS-based algorithms. Of the four integrated models, the SVR-GA model proved to be the model with the highest accuracy and best performance. It had AUROC values of 0.97 and 0.89 in the training and the testing phases, respectively.

Suggested Citation

  • Ali Nouh Mabdeh & A’kif Al-Fugara & Khaled Mohamed Khedher & Muhammed Mabdeh & Abdel Rahman Al-Shabeeb & Rida Al-Adamat, 2022. "Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms," Sustainability, MDPI, vol. 14(15), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9446-:d:878021
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    References listed on IDEAS

    as
    1. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
    2. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    3. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
    4. J. A. Tenreiro Machado & António M. Lopes, 2014. "Analysis of Forest Fires by means of Pseudo Phase Plane and Multidimensional Scaling Methods," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, June.
    5. K. Malarz & S. Kaczanowska & K. Kułakowski, 2002. "Are Forest Fires Predictable?," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 13(08), pages 1017-1031.
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    Cited by:

    1. Chaoxue Tan & Zhongke Feng, 2023. "Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China," Sustainability, MDPI, vol. 15(7), pages 1-17, April.

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