River flow prediction based on improved machine learning method: Cuckoo search-artificial neural network

• Main Authors: Che Wan Zanial, Wan Norsyuhada, Abdul Malek, Marlinda, Md. Reba, Mohd. Nadzri, Zaini, Nuratiah, Ahmed, Ali Najah, Sherif, Mohsen, Elshafie, Ahmed
• Format: Article
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2023
• Subjects: GE Environmental Sciences
• Online Access: http://eprints.utm.my/105142/1/MohdNadzriMdReba2023_RiverFlowPredictionBasedonImprovedMachine.pdf; http://eprints.utm.my/105142/; http://dx.doi.org/10.1007/s13201-022-01830-0
• Institution: Universiti Teknologi Malaysia

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.