Publication:
Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques

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dc.citedby1
dc.contributor.authorTofiq Y.M.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57924167700en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:36:01Z
dc.date.available2023-05-29T09:36:01Z
dc.date.issued2022
dc.descriptionDams; Decision trees; Forecasting; Forestry; Information management; Irrigation; Reservoir management; Reservoirs (water); Stream flow; Support vector machines; Water management; Water supply; Aswan high dam; Boosted tree regression; High dams; Inflow predictions; Intelligence models; Random forest modeling; Random forests; Streamflow prediction; Support vectors machine; Tree regression; Neural networks; accuracy assessment; algorithm; artificial intelligence; artificial neural network; forecasting method; numerical model; regression analysis; river flow; streamflow; support vector machine; Aswan Dam; Aswan [Egypt]; Egypten_US
dc.description.abstractThe development of a river inflow prediction is a prerequisite for dam reservoir management. Precise�forecasting leads to better irrigation water management, reservoir operation refinement, enhanced hydropower output and mitigation of risk of natural hazards such as flooding. Dam created reservoirs prove to be an essential source of water in arid and semi-arid regions. Over the years, Artificial Intelligence (AI) has been used for development of models for prediction of various natural variables in different engineering fields. Also, several AI models have been proved to be beneficial over the conventional models in efficient prediction of various natural variables. In this study, four AI models, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Boosted Tree Regression (BTR) were developed and trained over 130-years�of monthly historical rainfall data to forecast streamflow at Aswan High Dam, Egypt. The input parameters were selected according to the Autocorrelation Function (ACF) plot. The findings revealed that RF model outperformed other techniques and could provide precise�monthly streamflow prediction with the lowest RMSE (2.2395) and maximum WI (0.998462), R2 (0.9012). The input combination for the optimum RF model was Qt-1, Qt-11, and Qt-12 (i.e., one-, eleven- and twelve-months delay inputs). The optimum RF model provides a reliable source of data for inflow predictions, which allows improved utilization of water resources and long-term water resource planning and management. � 2022, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-022-03339-2
dc.identifier.epage6016
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85139615172
dc.identifier.spage5999
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85139615172&doi=10.1007%2fs11269-022-03339-2&partnerID=40&md5=4a352e723cedd79db8217e9d71b32c06
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26648
dc.identifier.volume36
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.titleOptimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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