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Reservoir evaporation prediction modeling based on artificial intelligence methods

Forecasting; 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; a...

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Main Authors: Allawi M.F., Othman F.B., Afan H.A., Ahmed A.N., Hossain M.S., Fai C.M., El-Shafie A.
Other Authors: 57057678400
Format: Article
Published: MDPI AG 2023
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spelling my.uniten.dspace-246422023-05-29T15:25:24Z Reservoir evaporation prediction modeling based on artificial intelligence methods Allawi M.F. Othman F.B. Afan H.A. Ahmed A.N. Hossain M.S. Fai C.M. El-Shafie A. 57057678400 36630785100 56436626600 57214837520 55579596900 57214146115 16068189400 Forecasting; 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 Malaysia The 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. Final 2023-05-29T07:25:24Z 2023-05-29T07:25:24Z 2019 Article 10.3390/w11061226 2-s2.0-85068858352 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068858352&doi=10.3390%2fw11061226&partnerID=40&md5=16ae83998132002bda3586dab3f6192f https://irepository.uniten.edu.my/handle/123456789/24642 11 6 1226 All Open Access, Gold MDPI AG Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Forecasting; 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 Malaysia
author2 57057678400
author_facet 57057678400
Allawi M.F.
Othman F.B.
Afan H.A.
Ahmed A.N.
Hossain M.S.
Fai C.M.
El-Shafie A.
format Article
author Allawi M.F.
Othman F.B.
Afan H.A.
Ahmed A.N.
Hossain M.S.
Fai C.M.
El-Shafie A.
spellingShingle Allawi M.F.
Othman F.B.
Afan H.A.
Ahmed A.N.
Hossain M.S.
Fai C.M.
El-Shafie A.
Reservoir evaporation prediction modeling based on artificial intelligence methods
author_sort Allawi M.F.
title Reservoir evaporation prediction modeling based on artificial intelligence methods
title_short Reservoir evaporation prediction modeling based on artificial intelligence methods
title_full Reservoir evaporation prediction modeling based on artificial intelligence methods
title_fullStr Reservoir evaporation prediction modeling based on artificial intelligence methods
title_full_unstemmed Reservoir evaporation prediction modeling based on artificial intelligence methods
title_sort reservoir evaporation prediction modeling based on artificial intelligence methods
publisher MDPI AG
publishDate 2023
_version_ 1806427370954948608
score 13.222552

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