Groundwater quality forecasting modelling using artificial intelligence: A review

Abstract This review paper closely explores the techniques and significances of the most potent artificial intelligence (AI) approaches in a concise and integrated way, specifically in the groundwater quality modelling and forecasting for its suitability in domestic usage. This paper systematically provides an extensive review of the four most used AI methods: artificial neural network (ANN), adaptive network-based fuzzy inference system (ANFIS), evolutionary algorithm (EA) and support vector machine (SVM), to reflect on the features and abilities while defining the greatest challenges throughout the process of providing desired results. Analysis among the four AI methods found that ANN performed better when handling a large number of data sets and accurately made predictions due to its ability to model complex non-linear and complex relationships, despite some weaknesses. The findings of this review demonstrate that the successful adoption of AI models is impacted by the appropriateness of input consideration, types of individual functions, the efficiency of performance metrics, etc. The outcomes from this study will be beneficial for groundwater development plans and contribute to the improvement of the AI applications in groundwater quality. Recommendations are presented in this study to strengthen the knowledge development towards improving the modelling structure in the mentioned area.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Indrajeet Chaubey,et al.  Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed , 2008 .

[3]  Ioannis K. Nikolos,et al.  Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation , 2011 .

[4]  J. Hernandez,et al.  Uncertainty Considerations in Calibration and Validation of Hydrologic and Water Quality Models , 2015 .

[5]  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 .

[6]  B. Datta,et al.  Identification of groundwater pollution sources using GA-based linked simulation optimization model , 2006 .

[7]  O. Bozorg Haddad,et al.  Real-Time Operation of Reservoir System by Genetic Programming , 2012, Water Resources Management.

[8]  Asaad Y. Shamseldin,et al.  Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP) , 2011, Environ. Model. Softw..

[9]  Daoliang Li,et al.  A review of hydrological/water-quality models , 2014 .

[10]  Daniel W Smith,et al.  A neural network model to predict the wastewater inflow incorporating rainfall events. , 2002, Water research.

[11]  Angus R. Simpson,et al.  Genetic algorithms compared to other techniques for pipe optimization , 1994 .

[12]  Yousry Mahmoud Ghazaw,et al.  Runoff forecasting by artificial neural network and conventional model , 2011 .

[13]  S. S. Mahapatra,et al.  Prediction of Water Quality Index Using Neuro Fuzzy Inference System , 2011 .

[14]  Hsu-Hwa Chang,et al.  Parameter Design for Operating Window Problems: An Example of Paper Feeder Design , 2011 .

[15]  Markus Disse,et al.  Fuzzy rule-based models for infiltration , 1993 .

[16]  Uzay Kaymak,et al.  Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system , 2006, Comput. Geosci..

[17]  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.

[18]  Murat Ay,et al.  Artificial Intelligence (AI) Studies in Water Resources , 2018 .

[19]  J. Adamowski,et al.  Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran , 2016, Stochastic Environmental Research and Risk Assessment.

[20]  Ashu Jain,et al.  Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks , 2004 .

[21]  Hisao Ishibuchi,et al.  Effect of rule weights in fuzzy rule-based classification systems , 2001, IEEE Trans. Fuzzy Syst..

[22]  Frédéric Alexandre,et al.  Connectionist-Symbolic Integration: From Unified to Hybrid Approaches , 1996 .

[23]  R. B. Rezaur,et al.  River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia , 2012, Water Resources Management.

[24]  J. Nadal,et al.  Learning in feedforward layered networks: the tiling algorithm , 1989 .

[25]  Özgür Kisi,et al.  Predicting groundwater level fluctuations with meteorological effect implications - A comparative study among soft computing techniques , 2013, Comput. Geosci..

[26]  Özgür Kisi,et al.  A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm , 2015, Appl. Math. Comput..

[27]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[28]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[29]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[30]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.

[31]  Z. Yousefi,et al.  Modeling of Ground Water Salinity on the Caspian Southern Coasts , 2010 .

[32]  R. Valbuena,et al.  A Simple Approach of Groundwater Quality Analysis, Classification, and Mapping in Peshawar, Pakistan , 2019, Environments.

[33]  Yu-Pin Lin,et al.  Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms , 2006 .

[34]  Sayed Farhad Mousavi,et al.  Modelling nitrate concentration of groundwater using Adaptive Neural-Based Fuzzy Inference System. , 2018 .

[35]  D. R. Baughman,et al.  Fundamental and Practical Aspects of Neural Computing , 1995 .

[36]  Kaan Yetilmezsoy,et al.  An adaptive neuro-fuzzy approach for modeling of water-in-oil emulsion formation , 2011 .

[37]  B. Saghafian,et al.  Impact of climate variation and human activities on groundwater quality in northwest of Iran , 2019, Journal of Water Supply: Research and Technology-Aqua.

[38]  Zbigniew W. Kundzewicz,et al.  Water resources for sustainable development , 1997 .

[39]  K. Gnana Sheela,et al.  Review on Methods to Fix Number of Hidden Neurons in Neural Networks , 2013 .

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

[41]  Faxin Wang,et al.  Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction , 2017 .

[42]  T. Singh,et al.  A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis , 2007 .

[43]  A. Jalalkamali,et al.  Adaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis , 2018 .

[44]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[45]  Z. Yousefi,et al.  Integration of artificial neural network and geographic information system applications in simulating groundwater quality , 2016 .

[46]  Mohamad Sakizadeh,et al.  Artificial intelligence for the prediction of water quality index in groundwater systems , 2016, Modeling Earth Systems and Environment.

[47]  Rudolf Kruse,et al.  How the learning of rule weights affects the interpretability of fuzzy systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[48]  Xiao-yun Zhang,et al.  Application of Artificial Neural Networks to Classify Water Quality of the Yellow River , 2008, ACFIE.

[49]  L. Medsker,et al.  Design and development of hybrid neural network and expert systems , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[50]  A. Soldati,et al.  Artificial neural network approach to flood forecasting in the River Arno , 2003 .

[51]  K. P. Singh,et al.  Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches , 2014, Environmental Science and Pollution Research.

[52]  Hossein Yousefi,et al.  Evaluating the suitability of different parameters for qualitative analysis of groundwater based on analytical hierarchy process , 2016 .

[53]  Elahe Fallah-Mehdipour,et al.  Prediction and simulation of monthly groundwater levels by genetic programming , 2013 .

[54]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[55]  Peter J. Fleming,et al.  The MATLAB genetic algorithm toolbox , 1995 .

[56]  Adem Erdoğan,et al.  A PWR reload optimisation code (XCore) using artificial neural networks and genetic algorithms , 2003 .

[57]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[58]  Kate Smith-Miles,et al.  A meta-learning approach to automatic kernel selection for support vector machines , 2006, Neurocomputing.

[59]  Shakeel Ahmed,et al.  Forecasting groundwater level using artificial neural networks. , 2009 .

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

[61]  Vahid Nourani,et al.  Geomorphology-based genetic programming approach for rainfall–runoff modeling , 2013 .

[62]  T. Bhattacharya,et al.  Assessment of groundwater quality and associated health risks: A case study of Ranchi city, Jharkhand, India , 2017 .

[63]  Enrico Zio,et al.  Approaching the inverse problem of parameter estimation in groundwater models by means of artificial neural networks , 1997 .

[64]  M. Çimen,et al.  Estimation of daily suspended sediments using support vector machines , 2008 .

[65]  R. Erol,et al.  A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games , 2017 .

[66]  Maged M. Hamed,et al.  Prediction of wastewater treatment plant performance using artificial neural networks , 2004, Environ. Model. Softw..

[67]  M. Bagheri,et al.  Application of artificial intelligence for the management of landfill leachate penetration into groundwater, and assessment of its environmental impacts , 2017 .

[68]  M. Yamanaka,et al.  Sulfur isotope constraint on the provenance of salinity in a confined aquifer system of the southwestern Nobi Plain, central Japan , 2006 .

[69]  Holger R. Maier,et al.  Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach , 2009 .

[70]  Knut Kvaal,et al.  Analysing Complex Sensory Data by Non-Linear Artificial Neural Networks , 1996 .

[71]  Mohamed L. Hambaba.  Intelligent hybrid system for data mining , 1996, IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[72]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[73]  Mustafa M. Aral,et al.  Genetic Algorithms in Search of Groundwater Pollution Sources , 1996 .

[74]  K. P. Sudheer,et al.  Rainfall‐runoff modelling using artificial neural networks: comparison of network types , 2005 .

[75]  Amanda J. C. Sharkey,et al.  On Combining Artificial Neural Nets , 1996, Connect. Sci..

[76]  Eleni G. Farmaki,et al.  Artificial Neural Networks in water analysis: Theory and applications , 2010 .

[77]  O. Ks.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation , 2004 .

[78]  M. Isazadeh,et al.  Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters , 2017, Environmental Earth Sciences.

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

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

[81]  Bithin Datta,et al.  Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management , 2012 .

[82]  A. C. Cadavid,et al.  Principal Components and Independent Component Analysis of Solar and Space Data , 2007, 0709.3263.

[83]  Ahmad Hajinezhad,et al.  ANN and ANFIS models to predict the performance of solar chimney power plants , 2015 .

[84]  J. Reed,et al.  Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing. , 1967, Journal of theoretical biology.

[85]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[86]  Keith L. Downing,et al.  Introduction to Evolutionary Algorithms , 2006 .

[87]  Yuri Javier Ccoicca.  Applications of support vector machines in the exploratory phase of petroleum and natural gas: a survey , 2013 .

[88]  Weibin Feng,et al.  Application of SVM Based on Principal Component Analysis to Credit Risk Assessment in Commercial Banks , 2009, 2009 WRI Global Congress on Intelligent Systems.

[89]  J. Yen,et al.  Fuzzy Logic: Intelligence, Control, and Information , 1998 .

[90]  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.

[91]  Taher Rajaee,et al.  Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review , 2020 .

[92]  Leonid V. Belyaev,et al.  Simulating Hybrid Connectionist Architectures , 1989, 1989 Winter Simulation Conference Proceedings.

[93]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[94]  Mehmet Irfan Yesilnacar,et al.  Neural network prediction of nitrate in groundwater of Harran Plain, Turkey , 2008 .

[95]  Tshilidzi Marwala,et al.  The Fuzzy Gene Filter: An Adaptive Fuzzy Inference System for Expression Array Feature Selection , 2010, IEA/AIE.

[96]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[97]  O. Kisi,et al.  SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment , 2012 .

[98]  Xinhong Wang,et al.  Earthquake Prediction Based on Levenberg-Marquardt Algorithm Constrained Back-Propagation Neural Network Using DEMETER Data , 2010, KSEM.

[99]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[100]  L. Darrell Whitley,et al.  An overview of evolutionary algorithms: practical issues and common pitfalls , 2001, Inf. Softw. Technol..

[101]  Christian W. Dawson,et al.  An artificial neural network approach to rainfall-runoff modelling , 1998 .

[102]  Richard S. Rosenberg,et al.  Simulation of genetic populations with biochemical properties. II. Selection of crossover probabilities. , 1970 .

[103]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[104]  Federico Girosi,et al.  Support Vector Machines: Training and Applications , 1997 .

[105]  Mac McKee,et al.  Applicability of statistical learning algorithms in groundwater quality modeling , 2005 .

[106]  Dmitri Roussinov,et al.  A Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation , 1998 .

[107]  Jimson Mathew,et al.  Machine Learning and Artificial Intelligence-Based Approaches , 2015 .

[108]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[109]  L. Shu,et al.  Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling , 2012, Hydrogeology Journal.

[110]  Fu Rong Yu,et al.  The Application of Artifical Neural Network in the Groundwater Quality Assessment in Industrial Park Catchment , 2012 .

[111]  Holger R. Maier,et al.  Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges , 2000 .

[112]  Majid Sartaj,et al.  Predicting Nitrate Concentration and Its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model , 2015, Environmental Modeling & Assessment.

[113]  Hugh M. Cartwright,et al.  Applications of artificial intelligence in chemistry , 1993 .

[114]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[115]  M. Ehrampoush,et al.  Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran , 2015 .

[116]  Amir Jalalkamali,et al.  Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters , 2015, Earth Science Informatics.

[117]  David B. Fogel,et al.  A history of evolutionary computation , 2018, Evolutionary Computation 1.

[118]  K. P. Singh,et al.  Support vector machines in water quality management. , 2011, Analytica chimica acta.

[119]  Yunes Mogheir,et al.  Application of Artificial Neural Networks Model as Analytical Tool for Groundwater Salinity , 2011 .

[120]  Dawei Han,et al.  Identification of Support Vector Machines for Runoff Modelling , 2004 .

[121]  Kuolin Hsu,et al.  Improved streamflow forecasting using self-organizing radial basis function artificial neural networks , 2004 .

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

[123]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[124]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[125]  Chittaranjan Ray,et al.  NEURAL NETWORKS FOR AGRICHEMICAL VULNERABILITY ASSESSMENT OF RURAL PRIVATE WELLS , 2000 .

[126]  Xilai Zheng,et al.  Surface water quality forecasting based on ANN and GIS for the Chanzhi Reservoir, China , 2010, The 2nd International Conference on Information Science and Engineering.

[127]  Barnali M. Dixon,et al.  Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis , 2005 .

[128]  Chi Zhang,et al.  A new water quality assessment model based on projection pursuit technique. , 2009, Journal of environmental sciences.

[129]  Donna M. Rizzo,et al.  Counterpropagation Neural Network for Stochastic Conditional Simulation: An Application with Berea Sandstone , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[130]  Jacques Cohen,et al.  A Survey of Computational Methods Used in Microarray Data Interpretation , 2006 .

[131]  Yue Liao,et al.  A Method of Water Quality Assessment Based on Biomonitoring and Multiclass Support Vector Machine , 2011 .

[132]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[133]  M. Franchini.  Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models , 1996 .

[134]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[135]  Natarajan Venkat Kumar,et al.  Analysis of Groundwater for Potability from Tiruchirappalli City Using Backpropagation ANN Model and GIS , 2010 .

[136]  Peiyue Li,et al.  Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China , 2012, Environmental Monitoring and Assessment.

[137]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[138]  J. Adamowski,et al.  Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction , 2017 .

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

[140]  J V Tu,et al.  Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.

[141]  Pedro J. Zufiria,et al.  Adaptive Power System Stabilizer Using ANFIS and Genetic Algorithms , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[142]  Mohammad Teshnehlab,et al.  Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter , 2009, Fuzzy Sets Syst..

[143]  S. Nash,et al.  Linear and Nonlinear Programming , 1987 .

[144]  Barnali M. Dixon.  Application Of Neuro-fuzzy Techniques To Predict Ground Water Vulnerability , 2002 .

[145]  E. Toth,et al.  Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .

[146]  Lutz Prechelt,et al.  Early Stopping - But When? , 2012, Neural Networks: Tricks of the Trade.

[147]  Ajith Abraham,et al.  Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews , 2007 .

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

[149]  Andrea Castelletti,et al.  An evaluation framework for input variable selection algorithms for environmental data-driven models , 2014, Environ. Model. Softw..

[150]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[151]  Veera Boonjing,et al.  Predicting SET50 Index Trend Using Artificial Neural Network and Support Vector Machine , 2015, IEA/AIE.

[152]  D. Anguita,et al.  K-fold generalization capability assessment for support vector classifiers , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[153]  Raymond C Rowe,et al.  Artificial intelligence in pharmaceutical product formulation: neural computing and emerging technologies , 1998 .

[154]  Randall Matignon.  Neural Network Modeling using SAS Enterprise Miner , 2005 .

[155]  Khaled S. Balkhair.  Aquifer parameters determination for large diameter wells using neural network approach , 2002 .

[156]  Omid Bozorg Haddad,et al.  Optimum operation of wells in coastal aquifers , 2011 .

[157]  Nouma Izeboudjen,et al.  A new classification approach for neural networks hardware: from standards chips to embedded systems on chip , 2012, Artificial Intelligence Review.

[158]  K. Brindha,et al.  Methods for Assessing the Groundwater Quality , 2019, GIS and Geostatistical Techniques for Groundwater Science.

[159]  Özgür Kişi,et al.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt , 2004 .

[160]  A. C. Martínez-Estudillo,et al.  Hybridization of evolutionary algorithms and local search by means of a clustering method , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[161]  Özgür Kisi,et al.  Importance of hybrid models for forecasting of hydrological variable , 2015, Neural Computing and Applications.

[162]  Nur Islami Bin Rahman.  Application of the Artificial Neural Network and Neuro-fuzzy System for Assessment of Groundwater Quality , 2015 .

[163]  R. Rooki,et al.  Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran , 2019, Environmental Earth Sciences.

[164]  V. Gholami,et al.  A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS) , 2017, Applied Water Science.

[165]  Philip M. Wolfe,et al.  Implementation of fuzzy logic systems and neural networks in industry , 1997 .

[166]  FRANCISCO SÁNCHEZ-MARTOS,et al.  Assessment of Groundwater Quality by Means of Self-Organizing Maps: Application in a Semiarid Area , 2002, Environmental management.

[167]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[168]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

[169]  Martin T. Hagan,et al.  Neural network design , 1995 .

[170]  D. Erbas,et al.  Genetic Algorithms for Optimizing the Remediation of Contaminated Aquifer , 2000 .