Publication:
Reservoir evaporation prediction modeling based on artificial intelligence methods

Simple item page
dc.citedby22
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorOthman F.B.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHossain M.S.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid36630785100en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:25:24Z
dc.date.available2023-05-29T07:25:24Z
dc.date.issued2019
dc.descriptionForecasting; Network architecture; Radial basis function networks; Tropics; Artificial intelligence methods; Climatic conditions; Climatic parameters; Prediction accuracy; Prediction model; Radial basis function neural networks; Support vector regression (SVR); Tropical environmental; Evaporation; accuracy assessment; artificial intelligence; climate conditions; climate effect; evaporation; hydrological modeling; methodology; prediction; reservoir; tropical environment; Johor; Johor River; Layang Reservoir; Malaysia; West Malaysiaen_US
dc.description.abstractThe current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1226
dc.identifier.doi10.3390/w11061226
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85068858352
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068858352&doi=10.3390%2fw11061226&partnerID=40&md5=16ae83998132002bda3586dab3f6192f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24642
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titleReservoir evaporation prediction modeling based on artificial intelligence methodsen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS