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

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. Howev...

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Vydáno v:Water resources management Ročník 38; číslo 11; s. 4137 - 4159
Hlavní autoři: Moeini, Ramtin, Nasiri, Kamran, Hosseini, Seyed Hossein
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.09.2024
Springer Nature B.V
Témata:
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
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Shrnutí: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.
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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-024-03856-2