RETRACTED ARTICLE: Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: a comparative study

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

[2]  Zhiyong Liu,et al.  Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting , 2014 .

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

[4]  A. Gani,et al.  A clustering model based on an evolutionary algorithm for better energy use in crop production , 2015, Stochastic Environmental Research and Risk Assessment.

[5]  Kunio Watanabe,et al.  Application of an artificial neural network to estimate groundwater level fluctuation , 2008 .

[6]  M. Razack,et al.  Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy , 2010 .

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

[8]  Ashwani Kumar,et al.  Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India , 2013 .

[9]  M. Mirzavand,et al.  A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods , 2015, Water Resources Management.

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

[11]  A. Ahmadi,et al.  Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran , 2015 .

[12]  Roslan Hashim,et al.  Time-lapse resistivity investigation of salinity changes at an ex-promontory land: a case study of Carey Island, Selangor, Malaysia , 2011, Environmental monitoring and assessment.

[13]  Groundwater level simulation using artificial neural network: a case study from Aghili plain, urban area of Gotvand, south-west Iran , 2013 .

[14]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence , 2009 .

[15]  Abbas Moghimbeigi,et al.  Prediction the groundwater level of Hamadan-Bahar Plain, west of Iran using support vector machines. , 2013, Journal of research in health sciences.

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

[17]  R. Ghazavi,et al.  Impact of Flood Spreading on Groundwater Level Variation and Groundwater Quality in an Arid Environment , 2012, Water Resources Management.

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

[19]  R. F. Medina,et al.  Occurrence of Entomopathogenic Fungi from Agricultural and Natural Ecosystems in Saltillo, México, and their Virulence Towards Thrips and Whiteflies , 2011, Journal of insect science.

[20]  Ping Feng,et al.  An Integrated Groundwater Management Mode Based on Control Indexes of Groundwater Quantity and Level , 2013, Water Resources Management.

[21]  Fawen Li,et al.  Risk Assessment of Groundwater and its Application. Part II: Using a Groundwater Risk Maps to Determine Control Levels of the Groundwater , 2014, Water Resources Management.

[22]  Shahaboddin Shamshirband,et al.  Soft computing methodologies for estimation of bridge girder forces with perforations under tsunami wave loading , 2014, Bulletin of Earthquake Engineering.

[23]  Dalibor Petković,et al.  Examination of tapered plastic multimode fiber-based sensor performance with silver coating for different concentrations of calcium hypochlorite by soft computing methodologies--a comparative study. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[24]  Shahaboddin Shamshirband,et al.  Potential of radial basis function based support vector regression for global solar radiation prediction , 2014 .

[25]  Kunio Watanabe,et al.  Daily groundwater level fluctuation forecasting using soft computing technique , 2007 .

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

[27]  M. Radzuan,et al.  Evaluating freshwater lens morphology affected by seawater intrusion using chemistry-resistivity integrated technique: a case study of two different land covers in Carey Island, Malaysia , 2013, Environmental Earth Sciences.

[28]  Javier Ferrer,et al.  Modeling Water Resources and River-Aquifer Interaction in the Júcar River Basin, Spain , 2014, Water Resources Management.

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

[30]  Soichi Nishiyama,et al.  A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff , 2007 .

[31]  Dalibor Petković,et al.  RETRACTED: Investigation of plasmonic studies on morphology of deposited silver thin films having different thicknesses by soft computing methodologies—A comparative study , 2014 .

[32]  I. Jolly,et al.  A review of groundwater–surface water interactions in arid/semi‐arid wetlands and the consequences of salinity for wetland ecology , 2008 .

[33]  A. Pourmirza,et al.  Effect of Cardinal Directions on Gall Morphology and Parasitization of the Gall Wasp, Cynips quercusfolii , 2011, Journal of insect science.