Concept of Artificial Intelligence and Its Applications in Groundwater Spatial Studies

[1]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[2]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[3]  Jan Adamowski,et al.  Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling , 2016 .

[4]  B. Faybishenko,et al.  In Situ Monitoring of Groundwater Contamination Using the Kalman Filter. , 2018, Environmental science & technology.

[5]  D. R. Sena,et al.  Effect of Calibration and Validation Decisions on Streamflow Modeling for a Heterogeneous and Low Runoff–Producing River Basin in India , 2019, Journal of Hydrologic Engineering.

[6]  Xiaojuan Li,et al.  Assessing the Impact of Building Volume on Land Subsidence in the Central Business District of Beijing with SAR Tomography , 2017 .

[7]  Yan-Fang Sang,et al.  A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series , 2012, Water Resources Management.

[8]  Klemen Kenda,et al.  Groundwater Modeling with Machine Learning Techniques: Ljubljana polje Aquifer , 2018, Proceedings.

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

[10]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[11]  Atiqur Rahman,et al.  A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India , 2008 .

[12]  Y. Hassanzadeh,et al.  Groundwater Remediation through Pump-Treat-Inject Technology Using Optimum Control by Artificial Intelligence (OCAI) , 2019, Water Resources Management.

[13]  Andrew E. Mercer,et al.  Artificial Neural Networks and Support Vector Machines: Contrast Study for Groundwater Level Prediction. , 2015 .

[14]  N. K. Goel,et al.  Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship , 2006 .

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

[16]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[17]  Yunes Mogheir,et al.  Improving the Accuracy of Artificial Intelligence - Based Groundwater Quality Models Using Clustering Technique - A Case Study , 2013 .

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

[19]  Özgür Kişi,et al.  Daily suspended sediment estimation using neuro-wavelet models , 2010 .

[20]  M. Demirci,et al.  MODELING OF GROUNDWATER LEVEL USING ARTIFICIAL INTELLIGENCE TECHNIQUES: A CASE STUDY OF REYHANLI REGION IN TURKEY , 2019, Applied Ecology and Environmental Research.

[21]  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.

[22]  Jun Yu,et al.  Occurrence assessment of earth fissure based on genetic algorithms and artificial neural networks in Su-Xi-Chang land subsidence area, China , 2014, Geosciences Journal.

[23]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..

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

[25]  Nevenka Djurovic,et al.  Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS , 2015, TheScientificWorldJournal.

[26]  Anthony J. Jakeman,et al.  Artificial Intelligence techniques: An introduction to their use for modelling environmental systems , 2008, Math. Comput. Simul..

[27]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

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

[29]  Devika Narain,et al.  Flexible timing by temporal scaling of cortical responses , 2017, Nature Neuroscience.

[30]  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 .

[31]  Johan A. K. Suykens,et al.  Artificial neural networks for modelling and control of non-linear systems , 1995 .

[32]  Gopal Krishan,et al.  Application of Artificial Neural Network for Groundwater Level Simulation in Amritsar and Gurdaspur Districts of Punjab, India , 2015 .

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

[34]  Omid Bozorg Haddad,et al.  Genetic Programming in Groundwater Modeling , 2014 .

[35]  Rudolf Kruse,et al.  Neuro-fuzzy systems for function approximation , 1999, Fuzzy Sets Syst..

[36]  Ian Millington,et al.  Artificial Intelligence for Games , 2006, The Morgan Kaufmann series in interactive 3D technology.

[38]  P. Döll,et al.  Challenges in developing a global gradient-based groundwater model (G3M v1.0) for the integration into a global hydrological model , 2019, Geoscientific Model Development.

[39]  A. Malik,et al.  Artificial neural network modeling of the river water quality—A case study , 2009 .

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

[41]  Emery Coppola,et al.  Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction , 2010, J. Comput. Civ. Eng..

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

[43]  Barnali M. Dixon,et al.  Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran , 2013 .

[44]  N. Copty,et al.  Modelling level change in lakes using neuro-fuzzy and artificial neural networks , 2009 .

[45]  J. Adamowski.  River flow forecasting using wavelet and cross‐wavelet transform models , 2008 .

[46]  A. W. Harbaugh.  MODFLOW-2005 : the U.S. Geological Survey modular ground-water model--the ground-water flow process , 2005 .

[47]  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 .

[48]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .

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