Water Temperature Prediction Using Improved Deep Learning Methods through Reptile Search Algorithm and Weighted Mean of Vectors Optimizer
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- Rana Muhammad Adnan Ikram
- School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
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- Reham R. Mostafa
- Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt
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- Zhihuan Chen
- Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430000, China
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- Kulwinder Singh Parmar
- Department of Mathematics, IKG Punjab Technical University, Kapurthala 144601, India
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- Ozgur Kisi
- Department of Civil Engineering, Technical University of Lübeck, 23562 Lübeck, Germany
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- Mohammad Zounemat-Kermani
- Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman 00076, Iran
書誌事項
- 公開日
- 2023-01-23
- 権利情報
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- https://creativecommons.org/licenses/by/4.0/
- DOI
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- 10.3390/jmse11020259
- 公開者
- MDPI AG
説明
<jats:p>Precise estimation of water temperature plays a key role in environmental impact assessment, aquatic ecosystems’ management and water resources planning and management. In the current study, convolutional neural networks (CNN) and long short-term memory (LSTM) network-based deep learning models were examined to estimate daily water temperatures of the Bailong River in China. Two novel optimization algorithms, namely the reptile search algorithm (RSA) and weighted mean of vectors optimizer (INFO), were integrated with both deep learning models to enhance their prediction performance. To evaluate the prediction accuracy of the implemented models, four statistical indicators, i.e., the root mean square errors (RMSE), mean absolute errors, determination coefficient and Nash–Sutcliffe efficiency were utilized on the basis of different input combinations involving air temperature, streamflow, precipitation, sediment flows and day of the year (DOY) parameters. It was found that the LSTM-INFO model with DOY input outperformed the other competing models by considerably reducing the errors of RMSE and MAE in predicting daily water temperature.</jats:p>
収録刊行物
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- Journal of Marine Science and Engineering
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Journal of Marine Science and Engineering 11 (2), 259-, 2023-01-23
MDPI AG
