Extended Estimation of daily inflow of Sefidroud dam using meta-heuristic algorithms combined with fuzzy neural inference system

Document Type : Research Article


1 PhD candidate, Department of Science and Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

2 Department of Water Engineering, University of Tabriz, Iran

3 3- MSc in Water Resources Management, and Manager of Guilan Dams

4 4- MSc in Construction Management, and Head of Sefidrood Dam and Powerhouse and Maintenance Department

5 MSc in Geotechnics, Mohaghegh Ardabili University, and expert of Guilan Regional Water Company


Estimating water inflows to water resource systems is crucial for effective planning and optimal allocation of water resources across various consumption sectors. This study proposes a novel approach that combines Meta Heuristic algorithms, namely Water Cycle Algorithms (WCA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Neural Network Algorithm (NNA), and Grasshopper Optimization Algorithm (GOA), with a Neural-Fuzzy System for training and updating parameters. The objective is to develop accurate models for predicting the daily inflow of the Sefidroud reservoir dam. Unlike gradient-based algorithms, this method overcomes the challenges associated with training. The Autocorrelation Function and Correlation function were utilized to select four features: dam lake area, reservoir volume, reservoir level of the dam during the previous 7 days, and inflow in the previous day. Various statistical indicators were employed to evaluate the performance of the developed models. In the test stage, the ANFIS-WCA model demonstrated superior performance with the lowest values of SI (0.0736), MAE (0.05048),  NRMSE (0.0736), and the highest value of R2 (0.9840). Based on the GPI index, the ANFIS-WCA model was identified as the best model, followed by ANFIS-NNA, ANFIS-GOA, and ANFIS-WOA models. Conversely, the ANFIS-GOA model exhibited the least accuracy. The results indicated that the ANFIS-WCA model outperformed the ANFIS-NNA model by 31% in terms of SI, and the ANFIS-GOA model by 1.6% in terms of SI. Furthermore, the GPI index revealed an improvement of up to 11% compared to the ANFIS-ANN model, and 20% compared to the ANFIS-GOA model. The high accuracy of the ANFIS-WCA model, compared to other hybrid models, highlights the effectiveness of the water cycle algorithm in combination with the ANFIS model. This approach proves to be a powerful tool for estimating the input discharge of Sefidroud dam, as it successfully avoids local optima.


Main Subjects

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