Machine Learning-Based Assessment of Watershed Morphometry in Makran

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...

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Збережено в:

Бібліографічні деталі
Автори:	Reza Derakhshani, Mojtaba Zaresefat, Vahid Nikpeyman, Amin GhasemiNejad, Shahram Shafieibafti, Ahmad Rashidi, Majid Nemati, Amir Raoof
Формат:	Artigo
Мова:	Inglês
Опубліковано:	MDPI AG 2023-03-01
Серія:	Land
Предмети:	watershed morphometry fuzzy analytic hierarchy process artificial neural networks support vector regression multivariate linear regression tectonics
Онлайн доступ:	https://www.mdpi.com/2073-445X/12/4/776
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Machine Learning-Based Assessment of Watershed Morphometry in Makran

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