Jianhua Ping
School of Water Conservancy and Environment Engineering,Zhengzhou University, Zhengzhou, Henan, 450001, China
Yu Qiang
School of Water Conservancy and Environment Engineering,Zhengzhou University, Zhengzhou, Henan, 450001, China
Ma Xixia
School of Water Conservancy and Environment Engineering,Zhengzhou University, Zhengzhou, Henan, 450001, China
ABSTRACT
Groundwater levels prediction is very important to groundwater resources evaluation and management. A combined model of chaos theory, wavelet and support vector machine was develop to overcome the limitations including challenges in determination of orders of nonlinear models and low prediction accuracy which the simulated accuracy is high in groundwater levels foresting. Firstly, groundwater level series were decomposed into different frequency components in application of wavelet analysis. Secondly, phase space was reconstructed using chaotic analysis. Thirdly, support vector machine (SVM) was used to predict each component. Finally, all components were merged into a model to predict groundwater levels. A case study, annual groundwater levels located in the Spallumcheen B aquifer situated in the Fortune Creek watershed surrounded by mountains in semi-arid areas within west interior British Columbia, Canada was employed to examine the combined model. The integrated model was evaluated by qualitative graphical method and three quantitative approaches comprising of NSE, PIBAS and RSR techniques. These evaluation values indicated the combined model high accuracy in groundwater level prediction and the model was valuable and useful for groundwater level forecasting.
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How to cite this article
Jianhua Ping, Yu Qiang and Ma Xixia, 2013. A Combination Model of Chaos, Wavelet and Support Vector Machine Predicting
Groundwater Levels and its Evaluation Using Three Comprehensive Quantifying Techniques. Information Technology Journal, 12: 3158-3163.
DOI: 10.3923/itj.2013.3158.3163
URL: https://scialert.net/abstract/?doi=itj.2013.3158.3163
DOI: 10.3923/itj.2013.3158.3163
URL: https://scialert.net/abstract/?doi=itj.2013.3158.3163
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