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

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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.

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Availability of Data and Materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, 81746-73441, Iran

    Ramtin Moeini, Kamran Nasiri & Seyed Hossein Hosseini

Authors
  1. Ramtin Moeini
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  2. Kamran Nasiri
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  3. Seyed Hossein Hosseini
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Contributions

Ramtin Moeini contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by Kamran Nasiri. Finally, the SARIMA model is calibrated and used by Seyed Hossein Hosseini. The manuscript was written by Ramtin Moeini. All authors read and approved the final manuscript.

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Correspondence to Ramtin Moeini.

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Ramtin Moeini, Kamran Nasiri, and Seyed Hossein Hosseini declare they have no financial interests.

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Moeini, R., Nasiri, K. & Hosseini, S.H. Predicting the Water Inflow Into the Dam Reservoir Using the Hybrid Intelligent GP-ANN- NSGA-II Method. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03856-2

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