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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Published: |
Springer Heidelberg
2023
|
| Subjects: | |
| Online Access: | http://eprints.um.edu.my/39148/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 (R-2). 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 R-2 value as compared to ANN model with R-2 of 0.900 at training stage and R-2 of 0.935 at testing stage. RMSE value, for ANN model, is 127.79 m(3)/s for training stage and 12.7 m(3)/s at testing stage. While for the proposed Hybrid CS-ANN model, RMSE value is equal to 121.7 m(3)/s for training stage and 10.95 m(3)/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. |
|---|