Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm
algorithm; artificial neural network; evaporation; optimization; uncertainty analysis; Kuala Terengganu; Kuantan; Malaysia; Pahang; Terengganu; West Malaysia; water; algorithm; Malaysia; uncertainty; Algorithms; Malaysia; Neural Networks, Computer; Uncertainty; Water
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2023
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my.uniten.dspace-269812023-05-29T17:38:21Z Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm Ehteram M. Panahi F. Ahmed A.N. Huang Y.F. Kumar P. Elshafie A. 57113510800 55368172500 57214837520 55807263900 57206939156 16068189400 algorithm; artificial neural network; evaporation; optimization; uncertainty analysis; Kuala Terengganu; Kuantan; Malaysia; Pahang; Terengganu; West Malaysia; water; algorithm; Malaysia; uncertainty; Algorithms; Malaysia; Neural Networks, Computer; Uncertainty; Water Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:38:21Z 2023-05-29T09:38:21Z 2022 Article 10.1007/s11356-021-16301-3 2-s2.0-85123442098 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123442098&doi=10.1007%2fs11356-021-16301-3&partnerID=40&md5=9b2eb136613a13cb031887466267087e https://irepository.uniten.edu.my/handle/123456789/26981 29 7 10675 10701 Springer Science and Business Media Deutschland GmbH Scopus |
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| description |
algorithm; artificial neural network; evaporation; optimization; uncertainty analysis; Kuala Terengganu; Kuantan; Malaysia; Pahang; Terengganu; West Malaysia; water; algorithm; Malaysia; uncertainty; Algorithms; Malaysia; Neural Networks, Computer; Uncertainty; Water |
| author2 |
57113510800 |
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57113510800 Ehteram M. Panahi F. Ahmed A.N. Huang Y.F. Kumar P. Elshafie A. |
| format |
Article |
| author |
Ehteram M. Panahi F. Ahmed A.N. Huang Y.F. Kumar P. Elshafie A. |
| spellingShingle |
Ehteram M. Panahi F. Ahmed A.N. Huang Y.F. Kumar P. Elshafie A. Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm |
| author_sort |
Ehteram M. |
| title |
Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm |
| title_short |
Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm |
| title_full |
Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm |
| title_fullStr |
Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm |
| title_full_unstemmed |
Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm |
| title_sort |
predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
| _version_ |
1806423999645745152 |
| score |
13.222552 |