Suspended sediment load prediction using long short-term memory neural network
article; Johor; linear regression analysis; long short term memory network; multilayer perceptron; particle resuspension; prediction; river
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2023
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my.uniten.dspace-258932023-05-29T17:05:27Z Suspended sediment load prediction using long short-term memory neural network AlDahoul N. Essam Y. Kumar P. Ahmed A.N. Sherif M. Sefelnasr A. Elshafie A. 56656478800 57203146903 57206939156 57214837520 7005414714 6505592467 16068189400 article; Johor; linear regression analysis; long short term memory network; multilayer perceptron; particle resuspension; prediction; river Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988�1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively. � 2021, The Author(s). Final 2023-05-29T09:05:27Z 2023-05-29T09:05:27Z 2021 Article 10.1038/s41598-021-87415-4 2-s2.0-85104157867 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104157867&doi=10.1038%2fs41598-021-87415-4&partnerID=40&md5=236aeb317a5c82d1db499a27177d2724 https://irepository.uniten.edu.my/handle/123456789/25893 11 1 7826 All Open Access, Gold, Green Nature Research Scopus |
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article; Johor; linear regression analysis; long short term memory network; multilayer perceptron; particle resuspension; prediction; river |
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56656478800 |
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56656478800 AlDahoul N. Essam Y. Kumar P. Ahmed A.N. Sherif M. Sefelnasr A. Elshafie A. |
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Article |
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AlDahoul N. Essam Y. Kumar P. Ahmed A.N. Sherif M. Sefelnasr A. Elshafie A. |
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AlDahoul N. Essam Y. Kumar P. Ahmed A.N. Sherif M. Sefelnasr A. Elshafie A. Suspended sediment load prediction using long short-term memory neural network |
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AlDahoul N. |
title |
Suspended sediment load prediction using long short-term memory neural network |
title_short |
Suspended sediment load prediction using long short-term memory neural network |
title_full |
Suspended sediment load prediction using long short-term memory neural network |
title_fullStr |
Suspended sediment load prediction using long short-term memory neural network |
title_full_unstemmed |
Suspended sediment load prediction using long short-term memory neural network |
title_sort |
suspended sediment load prediction using long short-term memory neural network |
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Nature Research |
publishDate |
2023 |
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1806427699649970176 |
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13.214268 |