Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method |
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| Authors: | Viet-Ha Nhu Khabat Khosravi James R Cooper Mahshid Karimi Ozgur Kisi Binh Thai Pham |
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| Institution: | 1. Geographic Information Science Research Group, Ton Duc Thang University , Ho Chi Minh City, Vietnam;2. Faculty of Environment and Labour Safety, Ton Duc Thang University , Ho Chi Minh City, Vietnam;3. Department of Watershed Management Engineering, Sari Agricultural Science and Natural Resources University , Sari, Iran;4. Department of Geography and Planning, School of Environmental Sciences, University of Liverpool , Liverpool, UK;5. School of Technology, Ilia State University , Tbilisi, Georgia  https://orcid.org/0000-0001-7847-5872;6. Department of Geotechnical Engineering, University of Transport Technology , Hanoi, Vietnam https://orcid.org/0000-0001-9707-840X |
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| Abstract: | ABSTRACT The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone. |
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| Keywords: | suspended sediment rivers artificial intelligence random forest random subspace support vector machine |
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![ORCID Icon]({"type":"image","src":"/templates/jsp/images/orcid.png"} "ORCID Icon")
https://orcid.org/0000-0001-7847-5872;6. Department of Geotechnical Engineering, University of Transport Technology , Hanoi, Vietnam