Predicting the Water Inflow Into the Dam Reservoir Using the Hybrid Intelligent GP-ANN- NSGA-II Method

• Published in: Water resources management Vol. 38; no. 11; pp. 4137 - 4159
• Main Authors: Moeini, Ramtin, Nasiri, Kamran, Hosseini, Seyed Hossein
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2024; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Sciences; Case studies; Civil Engineering; Correlation coefficient; Correlation coefficients; Dams; data collection; Datasets; Earth and Environmental Science; Earth Sciences; Environment; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; hybrids; Hydrogeology; Hydrology/Water Resources; Inflow; Methods; Neural networks; Reservoirs; Root-mean-square errors; Variables; water; Water inflow; Water resources; Water inflow prediction; NSGA-II algorithm; Artificial neural network; Zayandehroud dam; Genetic programming
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-024-03856-2

A key issue for effective management and operating of dam reservoirs is predicting the water inflow values into dam reservoir. To address this subject, here, genetic programming (GP) is used by proposing two cases. In the first case, water inflow values are predicted separately for each month. However, in the second case, these values are predicted simultaneously for all months. Furthermore, for each case, two approaches are proposed here. In the first approach, the hybrid method, called the ANN-NGSA-II method, is proposed to find proper input data sets. However, in the second approach, the useful input data sets are found automatically using the GP method. For comparison purpose, the ANN and SARIMA models are also used, to predict the water inflow values. As a case study, in this research, the Zayandehroud dam reservoir is selected. The results indicate that the ANN model outperforms both results of the GP and SARIMA methods. In other words, correlation coefficient (R 2 ), Nash Sutcliffe (NS), and root means square error (RMSE) values of ANN are 0.97, (0.88), 0.954 (0.87), and 17.19 (30.54) million cubic meters, respectively, for training (test) data set.