Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks

River sedimentation is a universal issue in a river catchment. It can affect the reservoir ability, the river flow, and dam structure including the hydropower capacity. Therefore, having multi-step ahead forecasting for the sediment load is beneficial in terms of research and applications. This stud...

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Main Authors: Solihin M.I., Hayder G., Maarif H.A.-Q., Khan Q.
Other Authors: 16644075500
Format: Conference Paper
Published: Springer Nature 2024
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spelling my.uniten.dspace-346362024-10-14T11:21:18Z Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks Solihin M.I. Hayder G. Maarif H.A.-Q. Khan Q. 16644075500 56239664100 45561462400 58309988500 Multi-step ahead forecasting NARX Neural networks River system Sediment load River sedimentation is a universal issue in a river catchment. It can affect the reservoir ability, the river flow, and dam structure including the hydropower capacity. Therefore, having multi-step ahead forecasting for the sediment load is beneficial in terms of research and applications. This study discusses and presents a case study in multi-step ahead forecasting for the sediment load using non-linear autoregressive with exogenous inputs (NARX) neural networks. We use sediment data that was recorded from 8 locations in the Ringlet reservoir (upstream sections) in Malaysia. The results suggest that the NARX neural networks have good capability to do multi-step ahead forecasting for sediment load in a recursive way (closed-loop mode) based on its past values and the past values of suspended solid and discharge. The model is evaluated with performance metrics yielding NSE = 0.99 (Nash�Sutcliffe efficiency coefficient) for both the training and test dataset, and RMSE (root means square error) of 0.22 and 0.25, respectively, training and test dataset. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. Final 2024-10-14T03:21:18Z 2024-10-14T03:21:18Z 2023 Conference Paper 10.1007/978-3-031-26580-8_9 2-s2.0-85161541241 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161541241&doi=10.1007%2f978-3-031-26580-8_9&partnerID=40&md5=747c7bedee9749dd81ad1b378eacb17d https://irepository.uniten.edu.my/handle/123456789/34636 45 50 Springer Nature Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Multi-step ahead forecasting
NARX
Neural networks
River system
Sediment load
spellingShingle Multi-step ahead forecasting
NARX
Neural networks
River system
Sediment load
Solihin M.I.
Hayder G.
Maarif H.A.-Q.
Khan Q.
Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks
description River sedimentation is a universal issue in a river catchment. It can affect the reservoir ability, the river flow, and dam structure including the hydropower capacity. Therefore, having multi-step ahead forecasting for the sediment load is beneficial in terms of research and applications. This study discusses and presents a case study in multi-step ahead forecasting for the sediment load using non-linear autoregressive with exogenous inputs (NARX) neural networks. We use sediment data that was recorded from 8 locations in the Ringlet reservoir (upstream sections) in Malaysia. The results suggest that the NARX neural networks have good capability to do multi-step ahead forecasting for sediment load in a recursive way (closed-loop mode) based on its past values and the past values of suspended solid and discharge. The model is evaluated with performance metrics yielding NSE = 0.99 (Nash�Sutcliffe efficiency coefficient) for both the training and test dataset, and RMSE (root means square error) of 0.22 and 0.25, respectively, training and test dataset. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
author2 16644075500
author_facet 16644075500
Solihin M.I.
Hayder G.
Maarif H.A.-Q.
Khan Q.
format Conference Paper
author Solihin M.I.
Hayder G.
Maarif H.A.-Q.
Khan Q.
author_sort Solihin M.I.
title Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks
title_short Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks
title_full Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks
title_fullStr Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks
title_full_unstemmed Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks
title_sort multi-step ahead time-series forecasting of sediment load using narx neural networks
publisher Springer Nature
publishDate 2024
_version_ 1814061130691117056
score 13.214268