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Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China
- Source :
- Water, Vol 7, Iss 8, Pp 4477-4495 (2015), Water, Volume 7, Issue 8, Pages 4477-4495
- Publication Year :
- 2015
- Publisher :
- MDPI AG, 2015.
-
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.
- Subjects :
- Engineering
reservoir
lcsh:Hydraulic engineering
Meteorology
Heuristic (computer science)
Geography, Planning and Development
forecast
Inflow
Aquatic Science
computer.software_genre
Biochemistry
lcsh:Water supply for domestic and industrial purposes
monthly inflow
lcsh:TC1-978
Genetic algorithm
genetic algorithm
support vector machine
hybrid method
Water Science and Technology
computer.programming_language
lcsh:TD201-500
Artificial neural network
business.industry
Predictive value
Reservoir operation
Support vector machine
PEARL (programming language)
Data mining
business
computer
artificial neural networks
Subjects
Details
- Language :
- English
- ISSN :
- 20734441
- Volume :
- 7
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- Water
- Accession number :
- edsair.doi.dedup.....cd1b43a8c603d189de718a97381e9f2d