A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Vahid Moosavi (), Mehdi Vafakhah (), Bagher Shirmohammadi () and Negin Behnia ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2013, vol. 27, issue 5, 1321 pages

Abstract: Artificial neural network (ANN) and Adaptive Neuro-Fuzzy 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 data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS 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 sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error 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 = 0.12, E = 0.93 and R 2 = 0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE = 2.07, E = 0.63 and R 2 = 0.91 for wavelet-ANFIS model for 4 months ahead). Copyright Springer Science+Business Media Dordrecht 2013

Keywords: Wavelet-ANFIS; Wavelet-ANN; Groundwater level; Forecasting; Mashhad plain (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:27:y:2013:i:5:p:1301-1321

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DOI: 10.1007/s11269-012-0239-2

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