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Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy

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dc.citedby45
dc.contributor.authorSapitang M.en_US
dc.contributor.authorRidwan W.M.en_US
dc.contributor.authorKushiar K.F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57215211508en_US
dc.contributor.authorid57218502036en_US
dc.contributor.authorid57212462702en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:08:37Z
dc.date.available2023-05-29T08:08:37Z
dc.date.issued2020
dc.descriptionalternative energy; data set; hydroelectric power plant; machine learning; power generation; precipitation intensity; scenario analysis; sustainable development; uncertainty analysis; water level; Malaysiaen_US
dc.description.abstractThe aim of this study is to accurately forecast the changes in water level of a reservoir located in Malaysia with two different scenarios; Scenario 1 (SC1) includes rainfall and water level as input and Scenario 2 (SC2) includes rainfall, water level, and sent out. Different time horizons (one day ahead to seven days) will be investigated to check the accuracy of the proposed models. In this study, four supervised machine learning algorithms for both scenarios were proposed such as Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR), Bayesian Linear Regression (BLR) and Neural Network Regression (NNR). Eighty percent of the total data were used for training the datasets while 20% for the dataset used for testing. The models' performance is evaluated using five statistical indexes; the Correlation Coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Relative Squared Error (RSE). The findings showed that among the four proposed models, the BLR model outperformed other models with R2 0.998952 (1-day ahead) for SC1 and BDTR for SC2 with R2 0.99992 (1-day ahead). With regards to the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models (BLR and BDTR). The results showed the value of 95PPU for both models in both scenarios (SC1 and SC2) fall into the range between 80% to 100%. As for the d-factor, all values in SC1 and SC2 fall below one. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6121
dc.identifier.doi10.3390/su12156121
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85089340528
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089340528&doi=10.3390%2fsu12156121&partnerID=40&md5=8382fbe8ea2f363992c452cf190c323c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25369
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleMachine learning application in reservoir water level forecasting for sustainable hydropower generation strategyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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