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Water 2015, 7(8), 4477-4495; doi:10.3390/w7084477

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
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Author to whom correspondence should be addressed.
Academic Editor: Miklas Scholz
Received: 24 June 2015 / Accepted: 27 July 2015 / Published: 17 August 2015
(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)
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Abstract

Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. View Full-Text
Keywords: monthly inflow; reservoir; forecast; artificial neural networks; support vector machine; genetic algorithm; hybrid method monthly inflow; reservoir; forecast; artificial neural networks; support vector machine; genetic algorithm; hybrid method
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cheng, C.-T.; Feng, Z.-K.; Niu, W.-J.; Liao, S.-L. Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China. Water 2015, 7, 4477-4495.

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