Machine Learning-Based Assessment of Watershed Morphometry in Makran

Reza Derakhshani (), Mojtaba Zaresefat, Vahid Nikpeyman, Amin GhasemiNejad, Shahram Shafieibafti, Ahmad Rashidi, Majid Nemati and Amir Raoof
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Reza Derakhshani: Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands
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
Majid Nemati: Department of Geology, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
Amir Raoof: Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands

Land, 2023, vol. 12, issue 4, 1-19

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.

Keywords: watershed morphometry; fuzzy analytic hierarchy process; artificial neural networks; support vector regression; multivariate linear regression; tectonics; Makran (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:4:p:776-:d:1110740

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