Authors: Vahid Moosavi Mehdi Vafakhah Bagher Shirmohammadi Negin Behnia
Publish Date: 2013/01/26
Volume: 27, Issue: 5, Pages: 1301-1321
Abstract
Artificial neural network ANN and Adaptive NeuroFuzzy Inference System ANFIS have an extensive range of applications in water resources management Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels The objective of this research is to compare several datadriven models for forecasting groundwater level for different prediction periods In this study a number of model structures for Artificial Neural Network ANN Adaptive NeuroFuzzy Inference System ANFIS WaveletANN and WaveletANFIS models have been compared to evaluate their performances to forecast groundwater level with 1 2 3 and 4 months ahead under two case studies in two subbasins It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting It has been also shown that the forecasts made by WaveletANFIS models are more accurate than those by ANN ANFIS and WaveletANN models This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trialanderror method The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series The prediction of these models is more accurate for 1 and 2 months ahead for example RMSE = 012 E = 093 and R 2 = 099 for waveletANFIS model for 1 month ahead than for 3 and 4 months ahead for example RMSE = 207 E = 063 and R 2 = 091 for waveletANFIS model for 4 months ahead
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