Surface Water Quality Model: Impacts of Influential Variables

AbstractConsidering all possible input (predictor) variables in a predictive water quality model is impractical owing to computational workload and complexity of the problem. Computational efficien...

[1]  Vincent Hull,et al.  Modelling dissolved oxygen dynamics in coastal lagoons , 2008 .

[2]  Kunlun Xin,et al.  Using Radial Basis Function Neural Networks to Calibrate Water Quality Model , 2008 .

[3]  Santosh Kumar Nanda,et al.  A Cascaded Fuzzy Inference System for Indian river water quality prediction , 2011, Adv. Eng. Softw..

[4]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[5]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification , 2000 .

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

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

[8]  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.

[9]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[10]  Mohamed Meselhy Eltoukhy,et al.  The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. , 2010, Journal of hazardous materials.

[11]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[12]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[13]  Andrea Castelletti,et al.  An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers , 2013 .

[14]  Il Won Seo,et al.  Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers , 2015 .

[15]  G. Sahoo,et al.  Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii , 2006 .

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

[17]  Holger R. Maier,et al.  Data splitting for artificial neural networks using SOM-based stratified sampling , 2010, Neural Networks.

[18]  Nikita Basant,et al.  Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water — A case study , 2010 .

[19]  Aaron C. Zecchin,et al.  Improving partial mutual information-based input variable selection by consideration of boundary issues associated with bandwidth estimation , 2015, Environ. Model. Softw..

[20]  P. Mageshkumar,et al.  Neural Network Modelling of TDS Concentrations in Cauvery River Water, Tamilnadu, India , 2012 .

[21]  Mohamad Javad Alizadeh,et al.  Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. , 2015, Marine pollution bulletin.

[22]  Fi-John Chang,et al.  Evolutionary artificial neural networks for hydrological systems forecasting , 2009 .

[23]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[24]  R. Hanley,et al.  Artificial neural network application for multi-ecosystem carbon flux simulation , 2005 .

[25]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[26]  Durdu Ömer Faruk.  A hybrid neural network and ARIMA model for water quality time series prediction , 2010, Eng. Appl. Artif. Intell..

[27]  Mohammad Firuz Ramli,et al.  Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. , 2012, Marine pollution bulletin.

[28]  I. Dimopoulos,et al.  Application of neural networks to modelling nonlinear relationships in ecology , 1996 .

[29]  Holger R. Maier,et al.  Empirical comparison of various methods for training feed‐Forward neural networks for salinity forecasting , 1999 .

[30]  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..

[31]  Bellie Sivakumar,et al.  Chaos theory in hydrology: important issues and interpretations , 2000 .

[32]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[33]  Wenyan Wu,et al.  A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks , 2013 .

[34]  Daniela Pessani,et al.  Importance of feature selection in decision-tree and artificial-neural-network ecological applications. Alburnus alburnus alborella: A practical example , 2010, Ecol. Informatics.

[35]  Jacek M. Zurada,et al.  Sensitivity analysis for minimization of input data dimension for feedforward neural network , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[36]  Yannis Dimopoulos,et al.  Use of some sensitivity criteria for choosing networks with good generalization ability , 1995, Neural Processing Letters.

[37]  Shuangyin Liu,et al.  Study of short-term water quality prediction model based on wavelet neural network , 2013, Math. Comput. Model..

[38]  Holger R. Maier,et al.  Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems , 2008, Environ. Model. Softw..

[39]  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..

[40]  Holger R. Maier,et al.  Optimal division of data for neural network models in water resources applications , 2002 .

[41]  Michele Scardi,et al.  Developing an empirical model of phytoplankton primary production: a neural network case study , 1999 .

[42]  J. Abaurrea,et al.  Trend analysis of water quality series based on regression models with correlated errors , 2011 .