IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9446-d878021.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9446/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/15/9446/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mirzaei, Mohsen & Jafari, Ali & Gholamalifard, Mehdi & Azadi, Hossein & Shooshtari, Sharif Joorabian & Moghaddam, Saghi Movahhed & Gebrehiwot, Kindeya & Witlox, Frank, 2020. "Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover," Land Use Policy, Elsevier, vol. 95(C).
    2. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    3. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    4. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    5. Jhih-Huang Wang & Gwo-Fong Lin & Ming-Jui Chang & I-Hang Huang & Yu-Ren Chen, 2019. "Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3759-3780, September.
    6. Shuheng Wang & Guohao Li & Yifan Bao, 2018. "A novel improved fuzzy support vector machine based stock price trend forecast model," Papers 1801.00681, arXiv.org.
    7. Sina Paryani & Mojgan Bordbar & Changhyun Jun & Mahdi Panahi & Sayed M. Bateni & Christopher M. U. Neale & Hamidreza Moeini & Saro Lee, 2023. "Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 837-868, March.
    8. Sri Lakshmi Sesha Vani Jayanthi & Venkata Reddy Keesara & Venkataramana Sridhar, 2022. "Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    9. Vivien Lai & Ali Najah Ahmed & M.A. Malek & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Ahmed El-Shafie & Amr El-Shafie, 2019. "Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms," Sustainability, MDPI, vol. 11(17), pages 1-26, August.
    10. Ahmed El-Shafie & Amr El-Shafie & Muhammad Mukhlisin, 2014. "New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2093-2107, June.
    11. Ozgur Kisi, 2015. "Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5109-5127, November.
    12. Yashon O. Ouma & Ditiro B. Moalafhi & George Anderson & Boipuso Nkwae & Phillimon Odirile & Bhagabat P. Parida & Jiaguo Qi, 2022. "Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors," Sustainability, MDPI, vol. 14(22), pages 1-31, November.
    13. Mohammed Falah Allawi & Ahmed El-Shafie, 2016. "Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4773-4788, October.
    14. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    15. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    16. Raúl Parada & Jordi Font & Jordi Casas-Roma, 2019. "Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs," Energies, MDPI, vol. 12(10), pages 1-21, May.
    17. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    18. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    19. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    20. Janusz Sowinski, 2021. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company," Energies, MDPI, vol. 14(2), pages 1-18, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9446-:d:878021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.