Publication:
Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir

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dc.contributor.authorHassan K.S.M.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorKoo C.H.en_US
dc.contributor.authorWeng T.K.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorElshafie A.H.K.A.en_US
dc.contributor.authorid57386567700en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57204843657en_US
dc.contributor.authorid57387317300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:42:07Z
dc.date.available2023-05-29T09:42:07Z
dc.date.issued2022
dc.description.abstractClimate change is a long-term change in the ordinary weather conditions that affects the local, regional and global climates. One of the response solutions to overcome this phenomenon is by managing water resources more efficiently. The reservoir is a major infrastructure to achieve water resource management and therefore it requires accurate water resources forecasting. The SVR and MLPNN models are introduced as a solution to achieve an efficient reservoir inflow forecasting. There are many input parameters that influence reservoir water flow but the 3 most important parameters are storage level, rainfall, and evaporation that need to be fed into the two models. There have been various model parameters tested such as kernel types in the SVR and the number of hidden layers and neurons in the MLPNN. Both models have proven their ability but however, the MLPNN with two hidden layers and 4 neurons in each layer had outperformed the SVR after being tested using four different performance tests. � 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-85990-9_4
dc.identifier.epage47
dc.identifier.scopus2-s2.0-85121815458
dc.identifier.spage33
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85121815458&doi=10.1007%2f978-3-030-85990-9_4&partnerID=40&md5=2c5df2f8728d911599f3a335588c19f8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27284
dc.identifier.volume322
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.titleForecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoiren_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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