Vehicular pollution modeling using artificial neural network technique: A review

Air quality models form one of the most important components of an urban air quality management plan. An effective air quality management system must be able to provide the authorities with information on the current and likely future trends, enabling them to make necessary assessments regarding the extent and type of the air pollution control management strategies to be implemented throughout the area. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Recently, statistical modeling tool such as artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, a review of the applications of ANN in vehicular pollution modeling under urban condition and basic features of ANN and modeling philosophy, including performance evaluation criteria for ANN based vehicular emission models have been described.

[1]  Abdullah M. Alsugair,et al.  Artificial neural network approach for pavement maintenance , 1998 .

[2]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[3]  Barak A. Pearlmutter,et al.  Automatic Learning Rate Maximization by On-Line Estimation of the Hessian's Eigenvectors , 1992, NIPS 1992.

[4]  Holger R. Maier,et al.  Neural network based modelling of environmental variables: A systematic approach , 2001 .

[5]  A. Leung,et al.  Prediction of maximum daily ozone level using combined neural network and statistical characteristics. , 2003, Environment international.

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  Mahesh A Rege,et al.  A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources. , 1996, Journal of the Air & Waste Management Association.

[8]  P. Viotti,et al.  Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia , 2002 .

[9]  Mukesh Khare,et al.  Line source emission modelling , 2002 .

[10]  S.M.Shiva Nagendra.  Modelling of vehicular exhaust emissions for assessing the air quality near urban roads using artificial neural network , 2002 .

[11]  Mukesh Khare,et al.  ARTIFICIAL NEURAL NETWORK BASED LINE SOURCE MODELS FOR VEHICULAR EXHAUST EMISSION PREDICTIONS OF AN URBAN ROADWAY , 2004 .

[12]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[13]  E. W. Kairiss,et al.  Hebbian synapses: biophysical mechanisms and algorithms. , 1990, Annual review of neuroscience.

[14]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[15]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[16]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[17]  Patricio Perez,et al.  Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile , 2001 .

[18]  R. Sivacoumar,et al.  LINE SOURCE MODEL FOR VEHICULAR POLLUTION PREDICTION NEAR ROADWAYS AND MODEL EVALUATION THROUGH STATISTICAL ANALYSIS , 1999 .

[19]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[20]  Saleh M. Al-Alawi,et al.  Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks , 2002, Environ. Model. Softw..

[21]  A. Comrie.  Comparing Neural Networks and Regression Models for Ozone Forecasting , 1997 .

[22]  M. Katz.  Validation of models , 2006 .

[23]  R. Sivacoumar,et al.  Comparison and Performance Evaluation of Models Used for Vehicular Pollution Prediction , 2001 .

[24]  L. Dawidowski,et al.  Artificial neural network for the identification of unknown air pollution sources , 1999 .

[25]  Wei-Zhen Lu,et al.  Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. , 2005, Chemosphere.

[26]  Stephen Dorling,et al.  Statistical surface ozone models: an improved methodology to account for non-linear behaviour , 2000 .

[27]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[28]  H. D. Block,et al.  Analysis of a Four-Layer Series-Coupled Perceptron. II , 1962 .

[29]  James A. Anderson,et al.  An Introduction To Neural Networks , 1998 .

[30]  K. K. Chaudhry,et al.  Vehicular pollution prediction modelling: a review of highway dispersion models , 2004 .

[31]  Rod Barratt,et al.  Atmospheric Dispersion Modelling: An Introduction to Practical Applications , 2001 .

[32]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[33]  Leo Breiman,et al.  [Neural Networks: A Review from Statistical Perspective]: Comment , 1994 .

[34]  S. Mendel,et al.  The use of a neural network for studying the relationship between air pollution and asthma-related emergency room visits. , 1998, Respiratory medicine.

[35]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[36]  Jorge Reyes,et al.  Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile , 2000 .

[37]  V. Prybutok,et al.  A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. , 1996, Environmental pollution.

[38]  Marija Zlata Boznar,et al.  A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain , 1993 .

[39]  Mark Dougherty,et al.  Forecasting carbon monoxide concentrations near a sheltered intersection using video surveillance and neural networks: A comment , 1997 .