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Machine Learning-Based Assessment of Watershed Morphometry in Makran

Author

Listed:
  • Reza Derakhshani

    (Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands
    Department of Geology, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Mojtaba Zaresefat

    (Copernicus Institute of Sustainable Development, Utrecht University, 3584CB Utrecht, The Netherlands)

  • Vahid Nikpeyman

    (Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands)

  • Amin GhasemiNejad

    (Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Shahram Shafieibafti

    (Department of Geology, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Ahmad Rashidi

    (Department of Earthquake Research, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
    Department of Seismotectonics, International Institute of Earthquake Engineering and Seismology, Tehran 19537-14453, Iran)

  • Majid Nemati

    (Department of Geology, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
    Department of Earthquake Research, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Amir Raoof

    (Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands)

Abstract

This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The study area was analyzed by extracting watersheds from a Digital Elevation Model (DEM) and calculating eight morphometric indices. The morphometric parameters were normalized using fuzzy membership functions to improve accuracy. The performance of the machine learning algorithms is evaluated by mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R 2 ) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R 2 value of 0.974, MSE of 4.14 × 10 −6 , and MAE of 0.0015. The results of the machine learning algorithms were compared to the tectonic characteristics of the area, indicating the potential for utilizing the ANN algorithm in similar investigations. This approach offers a novel way to assess watershed morphometry using ML techniques, which may have advantages over other approaches.

Suggested Citation

  • Reza Derakhshani & Mojtaba Zaresefat & Vahid Nikpeyman & Amin GhasemiNejad & Shahram Shafieibafti & Ahmad Rashidi & Majid Nemati & Amir Raoof, 2023. "Machine Learning-Based Assessment of Watershed Morphometry in Makran," Land, MDPI, vol. 12(4), pages 1-19, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:776-:d:1110740
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    References listed on IDEAS

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    1. Mahdis sadat Jalaee & Alireza Shakibaei & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Reza Derakhshani, 2021. "A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    2. Mohammad Mokhtari & Iraj Abdollahie Fard & Khaled Hessami, 2008. "Structural elements of the Makran region, Oman sea and their potential relevance to tsunamigenisis," 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. 47(2), pages 185-199, November.
    3. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
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