Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations

This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting. Five different GP and ANFIS models comprising various combinations of water table depth values from two stations, Bondville and Perry, are developed to forecast one-, two- and three-day ahead water table depths. The root mean square errors (RMSE), scatter index (SI), Variance account for (VAF) and coefficient of determination (R^2) statistics are used for evaluating the accuracy of models. Based on the comparisons, it was found that the GP and ANFIS models could be employed successfully in forecasting water table depth fluctuations. However, GP is superior to ANFIS in giving explicit expressions for the problem.

[1]  Ali Aytek,et al.  An application of artificial intelligence for rainfall-runoff modeling , 2008 .

[2]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[3]  Ozgur Kisi,et al.  Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation , 2007 .

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

[5]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[6]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[7]  Samuel O. Russell,et al.  Reservoir Operating Rules with Fuzzy Programming , 1996 .

[8]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[9]  Özlem Terzi,et al.  Fuzzy Logic Model Approaches to Daily Pan Evaporation Estimation in Western Turkey , 2004 .

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

[11]  O. Kisi,et al.  A genetic programming approach to suspended sediment modelling , 2008 .

[12]  Ping Li,et al.  Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China. , 2009 .

[13]  Ferenc Szidarovszky,et al.  A Hybrid Artificial Neural Network‐Numerical Model for Ground Water Problems , 2007, Ground water.

[14]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[15]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[16]  Ferenc Szidarovszky,et al.  Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions , 2003 .

[17]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[18]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[19]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[20]  M. Keskin,et al.  Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey / Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue , 2004 .

[21]  Cândida Ferreira.  Gene Expression Programming in Problem Solving , 2002 .

[22]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[23]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[24]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

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

[26]  Mohammad Ali Ghorbani,et al.  Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks , 2010, Comput. Geosci..

[27]  K. Hipel,et al.  Time series modelling of water resources and environmental systems , 1994 .

[28]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  Shaozhong Kang,et al.  Neural Networks to Simulate Regional Ground Water Levels Affected by Human Activities , 2008, Ground water.

[30]  Ferenc Szidarovszky,et al.  Multiobjective Analysis of a Public Wellfield Using Artificial Neural Networks , 2007, Ground water.

[31]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .

[32]  M. Bierkens.  Modeling water table fluctuations by means of a stochastic differential equation , 1998 .

[33]  A. Bárdossy,et al.  Development of a fuzzy logic-based rainfall-runoff model , 2001 .

[34]  Ozgur Kisi,et al.  Evolutionary fuzzy models for river suspended sediment concentration estimation. , 2009 .

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

[36]  Vladan Babovic,et al.  A Data Mining Approach to Modelling of Water Supply Assets , 2002 .

[37]  Ferenc Szidarovszky,et al.  A neural network model for predicting aquifer water level elevations , 2005, Ground water.

[38]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .

[39]  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.