River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network
One of the largest hydropower facilities currently in operation in Malaysia is the Terengganu hydroelectric facility. As a result, for hydropower generation to be sustainable, future water availability in hydropower plants must be known. Therefore, it is necessary to precisely estimate how the river...
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my.uniten.dspace-346512024-10-14T11:21:26Z River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network Zanial W.N.C.W. Malek M.B.A. Reba M.N.M. Zaini N. Ahmed A.N. Sherif M. Elshafie A. 57205239441 55636320055 57222067435 56905328500 57214837520 7005414714 16068189400 ANN Hybrid CS-ANN Hydropower Plant River flow Malaysia Biomimetics Hydroelectric power Hydroelectric power plants Learning algorithms Machine learning Neural networks Optimization Rivers Stream flow Artificial neural network modeling Case-studies Hybrid CS-artificial neural network Hydropower plants Machine learning methods Prediction-based River flow River flow prediction Stand -alone Statistical indices algorithm artificial neural network hydroelectric power plant machine learning precipitation intensity prediction river flow Mean square error One of the largest hydropower facilities currently in operation in Malaysia is the Terengganu hydroelectric facility. As a result, for hydropower generation to be sustainable, future water availability in hydropower plants must be known. Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. In this research, Artificial Neural Network (ANN) is integrated with a nature-inspired optimizer, namely Cuckoo search algorithm (CS-ANN). The performance of the proposed algorithm then will be examined based on statistical indices namely Root-Mean-Square Error (RSME) and Determination Coefficient (R2). Then, the accuracy of the proposed model will be then examined with the stand-alone Artificial Neural Network (ANN). The statistical indices results indicate that the proposed Hybrid CS-ANN model showed an improvement based on R2 value as compared to ANN model with R2 of 0.900 at training stage and R2 of 0.935 at testing stage. RMSE value, for ANN model, is 127.79 m3/s for training stage and 12.7 m3/s at testing stage. While for the proposed Hybrid CS-ANN model, RMSE value is equal to 121.7 m3/s for training stage and 10.95 m3/s for testing stage. The results revealed that the proposed model outperformed the stand-alone model in predicting the river flow with high level of accuracy. Although the proposed model could be applied in different case study, there is a need to tune the model internal parameters when applied in different case study. � 2022, The Author(s). Final 2024-10-14T03:21:26Z 2024-10-14T03:21:26Z 2023 Article 10.1007/s13201-022-01830-0 2-s2.0-85143329279 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143329279&doi=10.1007%2fs13201-022-01830-0&partnerID=40&md5=65d1a13be48b17ab91946931e8c58b6b https://irepository.uniten.edu.my/handle/123456789/34651 13 1 28 All Open Access Gold Open Access Springer Science and Business Media Deutschland GmbH Scopus |
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ANN Hybrid CS-ANN Hydropower Plant River flow Malaysia Biomimetics Hydroelectric power Hydroelectric power plants Learning algorithms Machine learning Neural networks Optimization Rivers Stream flow Artificial neural network modeling Case-studies Hybrid CS-artificial neural network Hydropower plants Machine learning methods Prediction-based River flow River flow prediction Stand -alone Statistical indices algorithm artificial neural network hydroelectric power plant machine learning precipitation intensity prediction river flow Mean square error |
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ANN Hybrid CS-ANN Hydropower Plant River flow Malaysia Biomimetics Hydroelectric power Hydroelectric power plants Learning algorithms Machine learning Neural networks Optimization Rivers Stream flow Artificial neural network modeling Case-studies Hybrid CS-artificial neural network Hydropower plants Machine learning methods Prediction-based River flow River flow prediction Stand -alone Statistical indices algorithm artificial neural network hydroelectric power plant machine learning precipitation intensity prediction river flow Mean square error Zanial W.N.C.W. Malek M.B.A. Reba M.N.M. Zaini N. Ahmed A.N. Sherif M. Elshafie A. River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network |
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One of the largest hydropower facilities currently in operation in Malaysia is the Terengganu hydroelectric facility. As a result, for hydropower generation to be sustainable, future water availability in hydropower plants must be known. Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. In this research, Artificial Neural Network (ANN) is integrated with a nature-inspired optimizer, namely Cuckoo search algorithm (CS-ANN). The performance of the proposed algorithm then will be examined based on statistical indices namely Root-Mean-Square Error (RSME) and Determination Coefficient (R2). Then, the accuracy of the proposed model will be then examined with the stand-alone Artificial Neural Network (ANN). The statistical indices results indicate that the proposed Hybrid CS-ANN model showed an improvement based on R2 value as compared to ANN model with R2 of 0.900 at training stage and R2 of 0.935 at testing stage. RMSE value, for ANN model, is 127.79 m3/s for training stage and 12.7 m3/s at testing stage. While for the proposed Hybrid CS-ANN model, RMSE value is equal to 121.7 m3/s for training stage and 10.95 m3/s for testing stage. The results revealed that the proposed model outperformed the stand-alone model in predicting the river flow with high level of accuracy. Although the proposed model could be applied in different case study, there is a need to tune the model internal parameters when applied in different case study. � 2022, The Author(s). |
| author2 |
57205239441 |
| author_facet |
57205239441 Zanial W.N.C.W. Malek M.B.A. Reba M.N.M. Zaini N. Ahmed A.N. Sherif M. Elshafie A. |
| format |
Article |
| author |
Zanial W.N.C.W. Malek M.B.A. Reba M.N.M. Zaini N. Ahmed A.N. Sherif M. Elshafie A. |
| author_sort |
Zanial W.N.C.W. |
| title |
River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network |
| title_short |
River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network |
| title_full |
River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network |
| title_fullStr |
River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network |
| title_full_unstemmed |
River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network |
| title_sort |
river flow prediction based on improved machine learning method: cuckoo search-artificial neural network |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2024 |
| _version_ |
1814060110184448000 |
| score |
13.222552 |