Proper Orthogonal Decomposition Performance to Determine the Inputs to the Artificial Neural Network for Prediction of Inflow into Alavian Dam

Subject Areas : Water Resource Management

Saber Moazami ¹ , Roohollah Noori ² , Mohammad Reza Vesali Naseh ³ , Abbas Akbarzadeh ⁴

1 - Assistant Professor, Civil Engineering, Environmental Science Research Center, Eslamshahr Branch, Islamic Azad University, Tehran, Iran.
2 - Assistant Professor of Environmental Engineering, Department of Civil Engineering, Arak University, Arak, Iran. *(Corresponding Author)
3 - Assistant Professor of Environment, Water Research Institute, Ministry of Energy, Tehran, Iran.
4 - Assistant Professor of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Tehran, Iran.

Received: 2016-10-30 Accepted : 2017-05-17 Published : 2020-09-22

Keywords: Monthly Inflow, Alavian Dam, Proper Orthogonal Decomposition, Artificial Neural Network,

Abstract :

Background and Objective: Dams play an important role in development of countries by drinking and agricultural water supply, flood control, hydropower energy supply and recreational purposes. Constructing a dam and making an artificial lake has an important effect on surrounding environment, so being able to forecast the inflow to the dam is an important issue for water resource management. Method: In this study artificial neural network (ANN) was applied to forecast the monthly inflow from Soofichai River to Alavian Dam. Regarding the huge amount of input data to ANN model and for optimizing its application, proper orthogonal decomposition (POD) was used in order to determine the best inputs for ANN model . Finally, the application of ANN and POD-ANN models was evaluated by determination coefficient (R2), mean absolute error (MAE) and average of absolute relative error (AARE). Findings: Results of ANN and POD-ANN models indicated that although ANN output is close to the observed values of inflow to the dam, but it has significant errors. POD-ANN model showed better results than ANN model for high values of inflow. In generall, comparing R2, MAE and AARE values of two models revealed that POD-ANN model had better performance in both calibration and verification steps in comparison with ANN model. R2, MAE and AARE in verification step of POD-ANN model were 0.93, 0.79, and 0.54, respectively. Discussion and Conclusion: Preprocessing data contributes to better performance of  POD-ANN than ANN model, especially in high values of inflow. Therefore, it can be concluded that applying data preprocessing and reducing inputs to ANN model enhances its performance.

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