Suspended sediment load prediction using long short-term memory neural network

Abstract 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...

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Autores principales:	Nouar AlDahoul, Yusuf Essam, Pavitra Kumar, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed Elshafie
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e5e511edf72b45ff8de066d897ce0527
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spelling	oai:doaj.org-article:e5e511edf72b45ff8de066d897ce05272021-12-02T14:20:43ZSuspended sediment load prediction using long short-term memory neural network10.1038/s41598-021-87415-42045-2322https://doaj.org/article/e5e511edf72b45ff8de066d897ce05272021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87415-4https://doaj.org/toc/2045-2322Abstract 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.Nouar AlDahoulYusuf EssamPavitra KumarAli Najah AhmedMohsen SherifAhmed SefelnasrAhmed ElshafieNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nouar AlDahoul Yusuf Essam Pavitra Kumar Ali Najah Ahmed Mohsen Sherif Ahmed Sefelnasr Ahmed Elshafie Suspended sediment load prediction using long short-term memory neural network
description	Abstract 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.
format	article
author	Nouar AlDahoul Yusuf Essam Pavitra Kumar Ali Najah Ahmed Mohsen Sherif Ahmed Sefelnasr Ahmed Elshafie
author_facet	Nouar AlDahoul Yusuf Essam Pavitra Kumar Ali Najah Ahmed Mohsen Sherif Ahmed Sefelnasr Ahmed Elshafie
author_sort	Nouar AlDahoul
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
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e5e511edf72b45ff8de066d897ce0527
work_keys_str_mv	AT nouaraldahoul suspendedsedimentloadpredictionusinglongshorttermmemoryneuralnetwork AT yusufessam suspendedsedimentloadpredictionusinglongshorttermmemoryneuralnetwork AT pavitrakumar suspendedsedimentloadpredictionusinglongshorttermmemoryneuralnetwork AT alinajahahmed suspendedsedimentloadpredictionusinglongshorttermmemoryneuralnetwork AT mohsensherif suspendedsedimentloadpredictionusinglongshorttermmemoryneuralnetwork AT ahmedsefelnasr suspendedsedimentloadpredictionusinglongshorttermmemoryneuralnetwork AT ahmedelshafie suspendedsedimentloadpredictionusinglongshorttermmemoryneuralnetwork
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Suspended sediment load prediction using long short-term memory neural network

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