Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China
Publication: Environmental Engineering Science
Volume 27, Issue Number 5
Abstract
A large dataset is generally needed when modeling hydrological processes. However, for developing countries such as China, datasets are often unavailable in remote areas. An attempt to apply a novel genetic programming (GP) technique was made to model the relationship between streamflow of the West Malian River and the impact of climate change in the northeastern part of China. Available annual streamflow and climatic data were used for training and testing of the GP model. Data from the years between 1982 and 2002 were used for automatic selection of the model relationship. Prediction of the model was undertaken for the period 2003–2006 and the results were compared with measured data. Predicted annual streamflow of the West Malian River agreed with measured data to an acceptable degree of accuracy even with a small amount of dataset. For comparison, a multilayer perceptron method with back propagation algorithm, a gray theory model, and a multiple linear regression model were selected to conduct the prediction with the same dataset. Results showed that the performance of GP method was generally better than other statistical methods such as multilayer perceptron, gray theory model, and multiple linear regression model. Further, the results also showed that the GP method is a useful tool for water resource management, especially in developing countries, to evaluate the potential impacts of climate change on the streamflow when large datasets are unavailable.
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Published In

Environmental Engineering Science
Volume 27 • Issue Number 5 • May 2010
Pages: 377 - 385
Copyright
Copyright 2010, Mary Ann Liebert, Inc.
History
Published online: 7 May 2010
Published in print: May 2010
Accepted: 7 March 2010
Received: 1 March 2009
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