Forecasting Groundwater Level Based on Wavelet Network Model Combined with Genetic Algorithm

Li Ying Wang; Wei Guo Zhao

doi:10.4028/www.scientific.net/AMR.113-116.195

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 113-116Forecasting Groundwater Level Based on Wavelet...

Forecasting Groundwater Level Based on Wavelet Network Model Combined with Genetic Algorithm

Abstract:

This paper proposed an improved wavelet network model (WNM) which combined with genetic algorithm (GA) to forecast groundwater level, GA is used to determine the weights and parameters of WNM, which can avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Compared to WNM, our results show that the GA-WNM predictor can reduce significantly both relative mean errors and root mean squared errors of predicted groundwater level. We demonstrate the feasibility of applying GA-WNM in groundwater level prediction and prove that GA-WNM is applicable and performs well for groundwater data analysis.

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Periodical:

Advanced Materials Research (Volumes 113-116)

Pages:

195-198

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.113-116.195

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Online since:

June 2010

Authors:

Li Ying Wang, Wei Guo Zhao

Keywords:

Artificial Neural Network (ANN), Genetic Algorithm (GA), Groundwater Level, Wavelet Network Model

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