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Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM

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dc.citedby4
dc.contributor.authorHayder G.en_US
dc.contributor.authorSolihin M.I.en_US
dc.contributor.authorNajwa M.R.N.en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid16644075500en_US
dc.contributor.authorid57463777300en_US
dc.date.accessioned2023-05-29T09:41:44Z
dc.date.available2023-05-29T09:41:44Z
dc.date.issued2022
dc.description.abstractKelantan river (Sungai Kelantan in Malaysia) basin is one of the essential catchments as it has a history of flood events. Numerous studies have been conducted in river basin modelling for the prediction of flow and mitigation of flooding events as well as water resource management. Therefore, having multi-step-ahead forecasting for river flow (RF) is of important research interest in this regard. This study presents four different approaches for multi-step-ahead forecasting for the Kelantan RF, using NARX (nonlinear autoregressive with exogenous inputs) neural networks and deep learning recurrent neural networks called LSTM (long short-term memory). The dataset used was obtained in monthly record for 29 years between January 1988 and December 2016. The results show that two recursive methods using NARX and LSTM are able to do multi-step-ahead forecasting on 52 series of test datasets with NSE (Nash-Sutcliffe efficiency coefficient) values of 0.44 and 0.59 for NARX and LSTM, respectively. For few-step-ahead forecasting, LSTM with direct sequence-to-sequence produces promising results with a good NSE value of 0.75 (in case of two-step-ahead forecasting). However, it needs a larger data size to have better performance in longer-stepahead forecasting. Compared with other studies, the data used in this study is much smaller. � 2022 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.doi10.2166/h2oj.2022.134
dc.identifier.epage59
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85125126410
dc.identifier.spage42
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125126410&doi=10.2166%2fh2oj.2022.134&partnerID=40&md5=17a97f07820673074f29fbe2ab9ff015
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27258
dc.identifier.volume5
dc.publisherIWA Publishingen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleH2Open Journal
dc.titleMulti-step-ahead prediction of river flow using NARX neural networks and deep learning LSTMen_US
dc.typeArticleen_US
dspace.entity.typePublication
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