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

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dc.citedby0
dc.contributor.authorSolihin M.I.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorMaarif H.A.-Q.en_US
dc.contributor.authorKhan Q.en_US
dc.contributor.authorid16644075500en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid45561462400en_US
dc.contributor.authorid58309988500en_US
dc.date.accessioned2024-10-14T03:21:18Z
dc.date.available2024-10-14T03:21:18Z
dc.date.issued2023
dc.description.abstractRiver 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-26580-8_9
dc.identifier.epage50
dc.identifier.scopus2-s2.0-85161541241
dc.identifier.spage45
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161541241&doi=10.1007%2f978-3-031-26580-8_9&partnerID=40&md5=747c7bedee9749dd81ad1b378eacb17d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34636
dc.pagecount5
dc.publisherSpringer Natureen_US
dc.sourceScopus
dc.sourcetitleAdvances in Science, Technology and Innovation
dc.subjectMulti-step ahead forecasting
dc.subjectNARX
dc.subjectNeural networks
dc.subjectRiver system
dc.subjectSediment load
dc.titleMulti-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networksen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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