[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117893-en":3,"doc-seo-117893-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117893,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning-Based Assessment of Watershed Morphometry in Makran","This study proposes an artificial intelligence framework for assessing watershed morphometry in the Makran subduction zones of South Iran and Pakistan. It integrates machine learning models, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), within a single workflow. Watersheds are derived from a digital elevation model, eight morphometric indices are computed, and fuzzy membership normalization is applied to enhance accuracy. Model performance is validated using MSE, MAE, and R² and interpreted alongside tectonic characteristics to support the ANN’s suitability for comparable analyses.","land  \nArticle  \nMachine Learning-Based Assessment of Watershed Morphometry in Makran  \nReza Derakhshani 1,2, *, Mojtaba Zaresefat 3, Vahid Nikpeyman 1, Amin GhasemiNejad 4, Shahram Shafieibafti 2, Ahmad Rashidi 5,6, Majid Nemati 2,5 and Amir Raoof 1  \nCitation: Derakhshani, R.; Zaresefat, M.; Nikpeyman, V.; GhasemiNejad, A.; Shafieibafti, S.; Rashidi, A.; Nemati, M.; Raoof, A. Machine Learning-Based Assessment of Watershed Morphometry in Makran. Land 2023, 12, 776. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/land12040776](10.3390/land12040776)  \nAcademic Editor: Chuanrong Zhang  \nReceived: 14 February 2023  \nRevised: 25 March 2023  \nAccepted: 27 March 2023  \nPublished: 29 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands  \n2 Department of Geology, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran  \n3 Copernicus Institute of Sustainable Development, Utrecht University, 3584CB Utrecht, The Netherlands  \n4 Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran  \n5 Department of Earthquake Research, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran  \n6 Department of Seismotectonics, International Institute of Earthquake Engineering and Seismology, Tehran 19537-14453, Iran  \n* Correspondence: [r.derakhshani@uu.nl](r.derakhshani@uu.nl)  \nAbstract: 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 (R2 ) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R2 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.  \nKeywords: watershed morphometry; fuzzy analytic hierarchy process; artificial neural networks; support vector regression; multivariate linear regression; tectonics; Makran  \n1. Introduction  \nWatershed morphometry is a crucial factor in determining the impact of tectonic processes on the landscape. By analyzing the shape and geometry of watersheds at a regional scale, we can identify the relative significance of tectonic deformation versus erosion in landscape evolution [1,2] . Understanding the impact of these geological forces on the morphology of watersheds and the development of drainage systems is essential, as it can have implications for sediment supply to river reaches and increase the risk of landslides [3] . Furthermore, by quantifying the morphotectonic situation of watersheds, we can gain insight into their evolution and assess the role of regional tectonic control in shaping their development [2,4–6] . Recent studies (e.g., [7–10]) have expanded our understanding of the rel","cbCaikHr8slmOVTW","https://ap.wps.com/l/cbCaikHr8slmOVTW","pdf",3145000,1,19,"English","en",105,"# Introduction\n## Watershed morphometry and tectonic impact\n## GIS, AHP/FAHP, and data-driven methods\n# Materials and Methods\n## Data extraction from DEM and morphometric indices\n## Fuzzy membership normalization\n## Machine learning models and evaluation metrics\n# Results and Discussion\n## Model performance comparison (ANN, SVR, MLR)\n## Linking morphometry with tectonic characteristics","[{\"question\":\"What machine learning models are used to assess watershed morphometry in Makran?\",\"answer\":\"The study integrates ANN, support vector regression (SVR), and multivariate linear regression (MLR) to predict and evaluate watershed morphometric behavior.\"},{\"question\":\"How is the morphometric input data prepared in the proposed workflow?\",\"answer\":\"Watersheds are extracted from a digital elevation model (DEM), eight morphometric indices are calculated, and values are normalized using fuzzy membership functions.\"},{\"question\":\"How is the model performance evaluated and validated?\",\"answer\":\"Performance is assessed using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R²) between the model output and the actual dataset.\"}]","Machine Learning-Based Assessment of Watershed Morphometry in Makran | 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