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Wavelet and adaptive neuro-fuzzy inference system conjunction model for groundwater level predicting in a coastal aquifer

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Abstract

Accurately predicting groundwater level (GWL) fluctuations is one of the most important issues for managing groundwater resources. In this study, the feasibility of predicting weekly GWL fluctuations in a coastal aquifer using the wavelet-adaptive neuro-fuzzy inference system (WANFIS) was investigated. WANFIS was a conjunction model that combined discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS). GWL data of two wells located in the coastal aquifer of eastern Laizhou bay, China, were used to establish WANFIS model. The performances of WANFIS model, along with ANFIS model, were assessed in terms of the following statistical indices, such as coefficient of correlation (R), root mean square error, and mean absolute relative error. Compared with the best ANFIS models, the best WANFIS model gave a better prediction. Moreover, it was found that wavelet transform positively affected the ANFIS’s predicting ability. In addition, the WANFIS model was also found to be superior to the best ANN model. This study indicated that WANFIS model was preferable and could be applied successfully due to its high accuracy and reliability for predicting GWL.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (41001013). The authors thank the anonymous reviewers for reading the manuscript and for the suggestions and critical comments.

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Authors and Affiliations

  1. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Donggang West Rd 320, Lanzhou, 730000, Gansu Province, China

    Xiaohu Wen, Qi Feng, Haijiao Yu, Jianhua Si, Zongqiang Chang & Haiyang Xi

  2. Advanced Environmental Technologies, LLC, Fort Collins, CO, 80525, USA

    Jun Wu

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  1. Xiaohu Wen
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  2. Qi Feng
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  3. Haijiao Yu
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  4. Jun Wu
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Correspondence to Xiaohu Wen.

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Wen, X., Feng, Q., Yu, H. et al. Wavelet and adaptive neuro-fuzzy inference system conjunction model for groundwater level predicting in a coastal aquifer. Neural Comput & Applic 26, 1203–1215 (2015). https://doi.org/10.1007/s00521-014-1794-7

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  • DOI: https://doi.org/10.1007/s00521-014-1794-7

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