Publication:
Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management

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dc.citedby26
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2024-10-14T03:18:35Z
dc.date.available2024-10-14T03:18:35Z
dc.date.issued2023
dc.description.abstractAs a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resourcesen_US
dc.description.abstractthey have benefited human cultures in a variety of ways, including enhanced human health, increased food production, water supply for domestic and industrial use, economic growth, irrigation, hydro-power generation, and flood control. This study aims to compare the application of deep learning and conventional machine learning algorithms for predicting daily reservoir inflow. Long short-term memory (LSTM) has been applied as a deep learning algorithm and boosted regression tree (BRT) has been implemented as a machine learning algorithm. Five statistical indices have been selected to evaluate the performance of the proposed models. The selected statistical measurements are mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). The findings showed that LSTM outperformed BRT with a significant difference in terms of accuracy. � 2023, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-023-03499-9
dc.identifier.epage3241
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85150457950
dc.identifier.spage3227
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85150457950&doi=10.1007%2fs11269-023-03499-9&partnerID=40&md5=0f69e50540855df76ef5bdddef7e2d98
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34239
dc.identifier.volume37
dc.pagecount14
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.subjectBoosted regression tree (BRT)
dc.subjectDokan dam
dc.subjectLong short-term memory (LSTM)
dc.subjectReservoir inflow
dc.subjectBrain
dc.subjectClimate change
dc.subjectEconomics
dc.subjectErrors
dc.subjectFlood control
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectMean square error
dc.subjectReservoirs (water)
dc.subjectWater conservation
dc.subjectWater management
dc.subjectWater supply
dc.subjectBoosted regression tree
dc.subjectBoosted regression trees
dc.subjectDokan dam
dc.subjectLong short-term memory
dc.subjectMachine learning algorithms
dc.subjectReservoir inflow
dc.subjectRoot mean square errors
dc.subjectStreamflow prediction
dc.subjectSustainable water supply
dc.subjectWater supply management
dc.subjectregression analysis
dc.subjectreservoir
dc.subjectsustainable development
dc.subjectwater management
dc.subjectwater resource
dc.subjectwater supply
dc.subjectLong short-term memory
dc.titleStreamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Managementen_US
dc.typeArticleen_US
dspace.entity.typePublication
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