Publication:
Deep learning neural network for time series water level forecasting

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dc.citedby2
dc.contributor.authorZaini N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorNorhisham S.en_US
dc.contributor.authorMardi N.H.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid54581400300en_US
dc.contributor.authorid57190171141en_US
dc.date.accessioned2023-05-29T09:12:18Z
dc.date.available2023-05-29T09:12:18Z
dc.date.issued2021
dc.descriptionDeep neural networks; Flood control; Forecasting; Learning systems; Long short-term memory; Mean square error; Offshore oil well production; Time series; Water levels; Coefficient of determination; Daily time series; Forecasting models; Learning neural networks; Learning techniques; Root mean square errors; Training and testing; Water level forecasting; Deep learningen_US
dc.description.abstractReliable forecasting of water level is essential for flood prevention, future planning and warning. This study proposed to forecast daily time series water level for Malaysia�s rivers based on deep learning technique namely long short-term memory (LSTM). The deep learning neural network is based on artificial neural network (ANN) and part of broader machine learning. In this study, forecasting models are developed for 1-h ahead of time at multiple lag time which are 1-h, 2-h and 3-h lag time denoted as LSTMt-1, LSTMt-2 and LSTMt-3, respectively. Forecasted water level is significant for determination of effected area, future planning and warning. Root mean square error (RMSE) and coefficient of determination (R2 ) are utilized to evaluate the performance of proposed forecasting models. An analysis of error in term of RMSE and R2 show that the proposed LSTMt-3 model outperformed other models for water level forecasting during training and testing phase. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-33-6311-3_3
dc.identifier.epage29
dc.identifier.scopus2-s2.0-85100739606
dc.identifier.spage22
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100739606&doi=10.1007%2f978-981-33-6311-3_3&partnerID=40&md5=ff2f3f59d26129037af4175a0e2be86b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26584
dc.identifier.volume132
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Civil Engineering
dc.titleDeep learning neural network for time series water level forecastingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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