A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions

[1]  Sungmoon Jung,et al.  Weighted error functions in artificial neural networks for improved wind energy potential estimation , 2013 .

[2]  Xiaomin Chen,et al.  Soil Water Simulation and Predication Using Stochastic Models Based on LS-SVM for Red Soil Region of China , 2011 .

[3]  Mohammad Reza Nikoo,et al.  Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction , 2011 .

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

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

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

[7]  Ozgur Kisi,et al.  Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey , 2009 .

[8]  G. Corzo,et al.  River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin , 2009 .

[9]  E. Doğan,et al.  Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. , 2009, Journal of environmental management.

[10]  Eungyu Park,et al.  A simple model for water table fluctuations in response to precipitation , 2008 .

[11]  Y. R. Satyaji Rao,et al.  Modelling groundwater levels in an urban coastal aquifer using artificial neural networks , 2008 .

[12]  B. Saugier,et al.  A Linking Test to reduce the number of hydraulic parameters necessary to simulate groundwater recharge in unsaturated soils , 2008 .

[13]  I. Rojas,et al.  Recursive prediction for long term time series forecasting using advanced models , 2007, Neurocomputing.

[14]  Amaury Lendasse,et al.  Methodology for long-term prediction of time series , 2007, Neurocomputing.

[15]  Mac McKee,et al.  Effect of missing data on performance of learning algorithms for hydrologic predictions: Implications to an imputation technique , 2007 .

[16]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[17]  András Bárdossy,et al.  Calibration of hydrological model parameters for ungauged catchments , 2006 .

[18]  Orazio Giustolisi,et al.  Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions , 2006 .

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

[20]  Mac McKee,et al.  Multi-time scale stream flow predictions: The support vector machines approach , 2006 .

[21]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[22]  Upmanu Lall,et al.  Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series , 2005 .

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

[24]  Amaury Lendasse,et al.  Direct and Recursive Prediction of Time Series Using Mutual Information Selection , 2005, IWANN.

[25]  S. Thomas Ng,et al.  A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial , 2005 .

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

[27]  Heinz G. Stefan,et al.  Dynamics of vertical mixing in a shallow lake with submersed macrophytes , 2005 .

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

[29]  Mac McKee,et al.  Support vectors–based groundwater head observation networks design , 2004 .

[30]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[31]  Shie-Yui Liong,et al.  FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES 1 , 2002 .

[32]  Alex Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[33]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

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

[35]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[36]  Marc F. P. Bierkens,et al.  Physical basis of time series models for water table depths , 2000 .

[37]  S. Lek,et al.  Predicting stream nitrogen concentration from watershed features using neural networks , 1999 .

[38]  A. Soldati,et al.  River flood forecasting with a neural network model , 1999 .

[39]  Robert J. Kuligowski,et al.  USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA 1 , 1998 .

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

[41]  R. Singh,et al.  Two-dimensional modelling of water table fluctuation in response to localised transient recharge , 1995 .

[42]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[43]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[44]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[45]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[46]  Luis A. Bastidas,et al.  Multiobjective analysis of chaotic dynamic systems with sparse learning machines , 2006 .

[47]  Tiesong Hu,et al.  A Modified Neural Network for Improving River Flow Prediction , 2005 .

[48]  John C. Platt,et al.  Fast Training of Support Vector Machines using Sequential Minimal Optimization , 2000 .

[49]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[50]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[51]  R. Klees Hydrology and Earth System Sciences Discussions Interactive comment on “ The bias in GRACE estimates of continental water storage variations ” , 2022 .