A framework development for predicting the longitudinal dispersion coefficient in natural streams using an artificial neural network

The main objective of the present investigation is to predict longitudinal dispersion coefficient (Kx) in natural streams using artificial neural network (ANN) technique based on most famous training functions such as Trainlm, Trainrp, Trainscg, Trainoss, and so on. To achieve the goal, hydraulic and geometric data (shear velocity, channel width, local flow depth, and mean longitudinal velocity) that are easily obtained in natural streams are used. First, we have tried to review the most well-known of published work in the field due to find out deficiencies of them. Second, new approach of ANN model based on the famous training functions is applied for predicting Kx in natural streams and then the best architectures for each training functions is selected by trial and error. Finally, Levenberg-Marquardt training function (Trainlm) is selected as the best choice for training the network parameters. Determination coefficient (R2) and mean absolute error for ANN (Trainrp) model were equal to 0.94 and 33 in the training and 0.95 and 30 in the testing steps, respectively. It is hoped that the presented methodology in the research, can be useful in river water quality management studies. © 2010 American Institute of Chemical Engineers Environ Prog, 2011.

[1]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[2]  Daniel W Smith,et al.  A neural network model to predict the wastewater inflow incorporating rainfall events. , 2002, Water research.

[3]  Henry Liu.  Predicting Dispersion Coefficient of Streams , 1977 .

[4]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[5]  L. L. Rogers,et al.  Optimal field-scale groundwater remediation using neural networks and the genetic algorithm. , 1995, Environmental science & technology.

[6]  Babak Nadjar Araabi,et al.  Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration , 2010 .

[7]  Hikmet Kerem Cigizoglu,et al.  Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods , 2008 .

[8]  Babak Omidvar,et al.  Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic , 2010, Expert Syst. Appl..

[9]  Mohammad Mehdi Ebadzadeh,et al.  An expert system for predicting longitudinal dispersion coefficient in natural streams by using ANFIS , 2009, Expert Syst. Appl..

[10]  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..

[11]  R. G. Godfrey,et al.  Stream dispersion at selected sites , 1970 .

[12]  Hugo B. Fischer,et al.  Discussion of Simple Method for Predicting Dispersion in Streams by Raul S. McQuivey and Thomas N. Keefer , 1975 .

[13]  C. Charalambous,et al.  Conjugate gradient algorithm for efficient training of artifi-cial neural networks , 1990 .

[14]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[15]  Mohammad Ali Abdoli,et al.  Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad , 2009 .

[16]  Il Won Seo,et al.  Predicting Longitudinal Dispersion Coefficient in Natural Streams , 1998 .

[17]  M. J. D. Powell,et al.  Restart procedures for the conjugate gradient method , 1977, Math. Program..

[18]  D J Choi,et al.  A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. , 2001, Water research.

[19]  Majid Dehghani,et al.  Predicting the Longitudinal Dispersion Coefficient Using Support Vector Machine and Adaptive Neuro-Fuzzy Inference System Techniques , 2009 .

[20]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[21]  Christopher Y Choi,et al.  Source tracking of microbial intrusion in water systems using artificial neural networks. , 2008, Water research.

[22]  Nouri Rouh Elah,et al.  PREDICTION OF MUNICIPAL SOLID WASTE GENERATION BY USE OF ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF MASHHAD , 2008 .

[23]  P. Gelder,et al.  Forecasting daily streamflow using hybrid ANN models , 2006 .

[24]  Kwok-wing Chau,et al.  Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River , 2006 .

[25]  Martin Cote,et al.  Dynamic modelling of the activated sludge process: Improving prediction using neural networks , 1995 .

[26]  Vijay P. Singh,et al.  Longitudinal dispersion coefficient in straight rivers , 2001 .

[27]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[28]  Azin Khosravi,et al.  The Iranian Vital Horoscope; Appropriate Tool to Collect Health Statistics in Rural Areas , 2009 .

[29]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[30]  Y. Kuo,et al.  Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. , 2004, Water research.

[31]  N. J. Ferreira,et al.  Artificial neural network technique for rainfall forecasting applied to the São Paulo region , 2005 .

[32]  Roohollah Noori,et al.  Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. , 2010, Journal of environmental management.

[33]  Roger A Falconer,et al.  Longitudinal dispersion coefficients in natural channels. , 2002, Water research.

[34]  Zekai Sen,et al.  Comment on "Longitudinal dispersion coefficients in natural channels". , 2004, Water research.

[35]  Ashu Jain,et al.  Comparative Analysis of Event-Based Rainfall-Runoff Modeling Techniques—Deterministic, Statistical, and Artificial Neural Networks , 2003 .

[36]  Vijay P. Singh,et al.  Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network , 2005 .

[37]  Young-Seuk Park,et al.  Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network. , 2003, Water research.