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

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.

[1]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[2]  Majid Kholghi,et al.  Comparison of Groundwater Level Estimation Using Neuro-fuzzy and Ordinary Kriging , 2009 .

[3]  S. Lallahem,et al.  On the use of neural networks to evaluate groundwater levels in fractured media , 2005 .

[4]  Murat Kankal,et al.  Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks , 2012, Environmental Monitoring and Assessment.

[5]  Aldo Drago,et al.  Use of the wavelet transform on hydro-meteorological data , 2002 .

[6]  Shakeel Ahmed,et al.  Comparison of FFNN and ANFIS models for estimating groundwater level , 2011 .

[7]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[8]  Tommy S. W. Wong,et al.  Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling , 2010 .

[9]  Özgür Kisi,et al.  Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations , 2011, Comput. Geosci..

[10]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[11]  Amir Jalalkamali,et al.  Groundwater modeling using hybrid of artificial neural network with genetic algorithm , 2011 .

[12]  A. K. Lohani,et al.  Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques , 2012 .

[13]  Vijay P. Singh,et al.  Scaling characteristics of precipitation data in conjunction with wavelet analysis. , 2010 .

[14]  O. Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for precipitation forecasting , 2007 .

[15]  O. Kisi,et al.  Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model , 2010 .

[16]  B. Krishna,et al.  Monthly Rainfall Prediction Using Wavelet Neural Network Analysis , 2013, Water Resources Management.

[17]  Turgay PartalT. Partal.  River flow forecasting using different artificial neural network algorithms and wavelet transform , 2009 .

[18]  Vahid Nourani,et al.  Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. , 2009, The Science of the total environment.

[19]  C. Shu,et al.  Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system , 2008 .

[20]  Narayan Sahoo,et al.  Hybrid neural modeling for groundwater level prediction , 2010, Neural Computing and Applications.

[21]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[22]  Y. Kuo,et al.  Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. , 2004, Water research.

[23]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[24]  Yves Meyer.  Wavelets - algorithms & applications , 1993 .

[25]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[26]  K. P. Sudheer,et al.  Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India , 2010 .

[27]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[28]  J. Adamowski.  Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis , 2008 .

[29]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[30]  Jan Adamowski,et al.  Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. , 2010 .

[31]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[32]  Stefano Alvisi,et al.  Fuzzy neural networks for water level and discharge forecasting with uncertainty , 2010, Environ. Model. Softw..

[33]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[34]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[35]  Y. R. Satyaji Rao,et al.  Modelling groundwater levels in an urban coastal aquifer using artificial neural networks , 2008 .

[36]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[37]  P. Barlow.  Ground water in freshwater-saltwater environments of the Atlantic coast , 2003 .

[38]  Nadipuram R. Prasad,et al.  Communications phase synchronization using the adaptive network fuzzy inference system (anfis) , 2000 .

[39]  Bernard De Baets,et al.  Comparison of data-driven TakagiSugeno models of rainfalldischarge dynamics , 2005 .

[40]  Jan Adamowski,et al.  Influence of the 11 year solar cycle on annual streamflow maxima in Southern Canada , 2012 .

[41]  Mahmud Güngör,et al.  Monthly total sediment forecasting using adaptive neuro fuzzy inference system , 2010 .

[42]  B. Bobée,et al.  Artificial neural network modeling of water table depth fluctuations , 2001 .

[43]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .