A review of the artificial intelligence methods in groundwater level modeling

Abstract This study is a review to the special issue on artificial intelligence (AI) methods for groundwater level (GWL) modeling and forecasting, and presents a brief overview of the most popular AI techniques, along with the bibliographic reviews of the experiences of the authors over past years, and the reviewing and comparison of the obtained results. Accordingly, 67 journal papers published from 2001 to 2018 were reviewed in the terms of the features and abilities of the modeling approaches, input data consideration, prediction time steps, data division, etc. From the reviewed papers it can be concluded that despite some weaknesses, if the AI methods properly be developed, they can successfully be used to simulate and forecast the GWL time series in different aquifers. Since some of the stages of the AI modeling are based on the experience or trial-and-error procedures, it is useful to review them in the special application on GWL modeling. Many partial and general results were achieved from the reviewed papers, which can provide applicable guidelines for researchers who want to perform similar works in this field. Several new ideas in the related area of research are also presented in this study for developing innovative methods and for improving the quality of the modeling.

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

[2]  Bijaya K. Panigrahi,et al.  An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India , 2014, Neurocomputing.

[3]  Vijay Singh,et al.  Forecasting of groundwater level in hard rock region using artificial neural network , 2009 .

[4]  Li-Chiu Chang,et al.  Prediction of monthly regional groundwater levels through hybrid soft-computing techniques , 2016 .

[5]  Shui-Hua Jiang,et al.  Prediction of groundwater levels using evidence of chaos and support vector machine , 2017 .

[6]  Ismail Yusoff,et al.  Simulation of groundwater level through artificial intelligence system , 2015, Environmental Earth Sciences.

[7]  Taher Rajaee,et al.  Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. , 2011, The Science of the total environment.

[8]  John Quilty,et al.  Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework , 2018, Journal of Hydrology.

[9]  Lu-Hsien Chen,et al.  Groundwater Level Prediction Using SOM-RBFN Multisite Model , 2010 .

[10]  Andrew E. Mercer,et al.  Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines , 2018, Environmental Modeling & Assessment.

[11]  Lu Wenxi,et al.  Comparison of three forecasting models for groundwater levels: a case study in the semiarid area of west Jilin Province, China , 2014 .

[12]  Shahaboddin Shamshirband,et al.  RETRACTED ARTICLE: Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: a comparative study , 2015, Natural Hazards.

[13]  Madan K. Jha,et al.  Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment , 2013, Hydrogeology Journal.

[14]  Taher Rajaee,et al.  Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model , 2015, Environmental Monitoring and Assessment.

[15]  Rajandrea Sethi,et al.  Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon , 2012, Eng. Appl. Artif. Intell..

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

[17]  Lu-Hsien Chen,et al.  Application of Integrated Back-Propagation Network and Self-Organizing Map for Groundwater Level Forecasting , 2011 .

[18]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[19]  Hossein Sedghi,et al.  Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran , 2011 .

[20]  Mehdi Vafakhah,et al.  Application of Several Data-Driven Techniques for Predicting Groundwater Level , 2012, Water Resources Management.

[21]  Ozgur Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations , 2012 .

[22]  Ravinesh C. Deo,et al.  Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China , 2017, Water Resources Management.

[23]  Guanhui Cheng,et al.  Groundwater level prediction using a SOM-aided stepwise cluster inference model. , 2016, Journal of environmental management.

[24]  K. P. Sudheer,et al.  Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites , 2015, Water Resources Management.

[25]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[26]  Rahim Barzegar,et al.  Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. , 2017, The Science of the total environment.

[27]  Ozgur Kisi,et al.  Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review , 2014 .

[28]  Yaonan Zhang,et al.  Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions , 2014, Water Resources Management.

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

[30]  X. Wen,et al.  Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China , 2017 .

[31]  D. Whittemore,et al.  Interpretation of Water Level Changes in the High Plains Aquifer in Western Kansas , 2012, Ground water.

[32]  Kourosh Mohammadi,et al.  Groundwater Table Estimation Using MODFLOW and Artificial Neural Networks , 2009 .

[33]  Manfred Koch,et al.  Groundwater level fluctuations simulation and prediction by ANFIS- and hybrid Wavelet-ANFIS/ fuzzy C-means (FCM) clustering models: Application to the Miandarband plain , 2018 .

[34]  J. Adamowski,et al.  Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models , 2015, Hydrogeology Journal.

[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]  M. Nakhaei,et al.  Erratum to: Optimal design of BP algorithm by ACOR model for groundwater-level forecasting: A case study on Shabestar plain, Iran , 2016, Arabian Journal of Geosciences.

[38]  Bingjing Zhang,et al.  Simulation and uncertainty analysis for groundwater levels using radial basis function neural network and support vector machine models , 2017 .

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

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

[41]  Rathinasamy Maheswaran,et al.  Comparative study of different wavelets for hydrologic forecasting , 2012, Comput. Geosci..

[42]  Kang-Kun Lee,et al.  A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions , 2016, Computational Geosciences.

[43]  Paulin Coulibaly,et al.  Improving groundwater level forecasting with a feedforward neural network and linearly regressed projected precipitation , 2008 .

[44]  Samad Emamgholizadeh,et al.  Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) , 2014, Water Resources Management.

[45]  K. Chau,et al.  Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers , 2015 .

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

[47]  Mohammad Vaghefi,et al.  Forecasting Groundwater Level in Shiraz Plain Using Artificial Neural Networks , 2012 .

[48]  Vahid Nourani,et al.  Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method , 2016 .

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

[50]  Madan K. Jha,et al.  Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater , 2015 .

[51]  Taher Rajaee,et al.  Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine , 2017 .

[52]  Mosayeb Afshari Igder,et al.  Long-Term Groundwater-Level Forecasting in Shallow and Deep Wells Using Wavelet Neural Networks Trained by an Improved Harmony Search Algorithm , 2018 .

[53]  Özgür Kisi,et al.  Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff , 2013, Appl. Soft Comput..

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

[55]  Rathinasamy Maheswaran,et al.  Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics , 2013, Comput. Geosci..

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

[58]  Dimitri P. Solomatine.  Data‐Driven Modeling and Computational Intelligence Methods in Hydrology , 2006 .

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

[60]  Mehdi Vafakhah,et al.  A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods , 2013, Water Resources Management.

[61]  Mehdi Vafakhah,et al.  Optimization of Wavelet-ANFIS and Wavelet-ANN Hybrid Models by Taguchi Method for Groundwater Level Forecasting , 2014 .

[62]  Taher Rajaee,et al.  Forecasting of chlorophyll-a concentrations in South San Francisco Bay using five different models , 2015 .

[63]  Vahid Nourani,et al.  Spatiotemporal Groundwater Level Forecasting in Coastal Aquifers by Hybrid Artificial Neural Network-Geostatistics Model: A Case Study , 2011 .

[64]  Huan Wang,et al.  A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida , 2015, Water Resources Management.

[65]  Parthasarathy Ramachandran,et al.  Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India : Analysis of comparative performances of SVR, ANN and LRM , 2018 .

[66]  Shie-Yui Liong,et al.  Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest , 2016 .

[67]  Vahid Nourani,et al.  An ANN‐based model for spatiotemporal groundwater level forecasting , 2008 .

[68]  Holger R. Maier,et al.  Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling , 2014, Environ. Model. Softw..

[69]  Z. Hou,et al.  A comparative study of shallow groundwater level simulation with WA–ANN and ITS model in a coastal island of south China , 2015, Arabian Journal of Geosciences.

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

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

[72]  Vahid Nourani,et al.  Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling , 2015 .

[73]  Evdokia Tapoglou,et al.  A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation , 2014 .

[74]  Alexander Chatzigeorgiou,et al.  Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management , 2018, Water Resources Management.

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

[76]  Heesung Yoon,et al.  Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors , 2018, Hydrogeology Journal.

[77]  Stefan Broda,et al.  Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX) , 2018, Journal of Hydrology.

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

[79]  Juan Chang,et al.  Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model , 2015 .

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

[81]  Minghui Jiang,et al.  Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis , 2018, Geotechnical and Geological Engineering.