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

Ramtin Moeini (), Kamran Nasiri () and Seyed Hossein Hosseini ()
Additional contact information
Ramtin Moeini: University of Isfahan
Kamran Nasiri: University of Isfahan
Seyed Hossein Hosseini: University of Isfahan

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 11, No 9, 4137-4159

Abstract: Abstract 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 (R2), 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.

Keywords: Water inflow prediction; Genetic programming; Artificial neural network; NSGA-II algorithm; Zayandehroud dam (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-03856-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:11:d:10.1007_s11269-024-03856-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-03856-2

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().