Modeling and Prediction of Hourly Ambient Ozone () and Oxides of Nitrogen () Concentrations Using Artificial Neural Network and Decision Tree Algorithms for an Urban Intersection in India
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Volume 20, Issue 4
Abstract
The present study attempts to predict hourly ozone () and oxides of nitrogen () concentrations near a traffic intersection in megacity Delhi, India, using artificial neural network (ANN) with the Levenberg–Maquardt (LM) algorithm and decision tree algorithms [e.g., reduced error pruning tree (REPTree) and M5 P tree model]. The hourly averages of input variables of meteorological, traffic volume, and transport emissions along with target values of monitored ambient air concentrations of and oxides of nitrogen were used for model development. The LM, REPTree, and M5 P algorithm models were developed by training, validation, and testing of input and target data. Statistical agreement between observed and predicted values is assessed by coefficient of correlation (CC), mean square error (MSE), root mean square error (RMSE), normalized mean square Error (NMSE), and Nash–Sutcliffe efficiency index (N-S Index). Results show that the performance of the M5 P model is superior to ANN and REPTree models studied for prediction of and at a highly urbanized traffic intersection.
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Acknowledgments
We are grateful to Central Pollution Control Board (CPCB), Central Road Research Institute (CRRI), and Transport Department, New Delhi for making suitable data freely available and also Indian Meteorological Department for providing essential data as per the norms. We also extend our gratitude to S. K. Guttikunda for providing mixing height data for New Delhi.
References
ARAI (The Automotive Research Association of India). (2007). “Air quality monitoring project-Indian clean air programme (ICAP).”. Pune, India.
Biswas, J., Upadhyay, E., Nayak, M., and Yadav, A. K. (2011). “An analysis of ambient air quality conditions over Delhi, India from 2004 to 2009.” Atmos. Clim. Sci., 1(4), 214–221.
Bose, N. K., and Liang, P. (1998). Neural network fundamentals with graphs, algorithms and applications, Tata McGraw Hill, New Delhi, India.
Boznar, M., Lesjak, M., and Malker, P. (1993). “A neural network based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain.” Atmos. Environ., 27B(2), 221–230.
Burney, S. M. A., Jilani, T. A., and Ardil, C. (2008). “Levenberg-Marquardt algorithm for Karachi stock exchange share rates forecasting.” World Acad. Sci. Eng. Technol., 2(4), 171–176.
Cai, M., Yin, Y., and Xie, M. (2009). “Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach.” Transp. Res. Part D, 14(1), 32–41.
Chen, T., and March, C. F. (1971). “Effect of highway configurations on environmental problems dynamics of highway associated air pollution.” Second Int. Clean Air Congress, H. M. Englund and T. Berry, eds., Academic Press, New York, 35–40.
Chiu, S. (1994). “Fuzzy model identification based on cluster estimation.” J. Intell. Fuzzy Syst., 2(3), 267–278.
Comrie, A. C. (1997). “Comparing neural networks and regression model for ozone forecasting.” J. Air Waste Manage. Assoc., 47(6), 653–663.
CRRI (Central Road Research Institute). (1998). “Evaluation of emission characteristics and compliance of emission standards for in-use petrol driven vehicles and critical appraisal of the efficacy of the existing pollution checking system in Delhi.” Environment and Road Traffic Safety Div., New Delhi, India.
Daud, M. N. R., and Corne, D. W. (2007). “Human readable rule inductionin medical data mining: A survey of existing algorithms.” Proc., WSEAS European Computing Conf., Vol. 1, Springer LNEE, Athens, Greece, 787–798.
Dimopoulos, I., Chronopoulos, J., Sereli, A. C., and Lek, S. (1999). “Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece).” Ecol. Modell., 120(2–3), 157–165.
Dorzdowicz, B., Benz, S. J., Sonta, A. S. M., and Scenna, N. J. (1997). “A neural network based model for the analysis of carbon monoxide concentration in the urban area of Rosario.” Air pollution, H. Power, T. Tirabassis, and C. A. Brebbia, eds., Vol. 5, Computational Mechanics, Southampton, Boston, 677–685.
Esplin, G. L. (1995). “Approximate explicit solution to the general line source problem.” Atmos. Environ., 29(12), 1459–1463.
Gardner, M. W., and Dorling, S. R. (1996). “Neural network modelling of the influence of local meteorology on surface ozone concentrations.” Proc., Int. Conf. on Geo Computation, Univ. of Leeds, U.K., 359–370.
Gardner, M. W., and Dorling, S. R. (1998). “Artificial neural networks: the multilayer perceptron: A review of applications in atmospheric sciences.” Atmos. Environ., 32(14–15), 2627–2636.
Gardner, M. W., and Dorling, S. R. (1999). “Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London.” Atmos. Environ., 33(5), 709–719.
Gardner, M. W., and Dorling, S. R. (2000). “Statistical surface ozone models: an improved methodology to account for non-linear behaviour.” Atmos. Environ., 34(1), 21–34.
Gokhale, S., and Khare, M. (2005). “A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments.” Atmos. Environ., 39(22), 4025–4040.
Goyal, M. K., and Ojha, C. S. P. (2011). “Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree.” Water Resour. Manage., 25(9), 2177–2195.
Gurjar, B. R., and Lelieveld, J. (2005). “New directions: Megacities and global change.” Atmos. Environ., 39(2), 391–393.
Gurjar, B. R., van Aardenne, J. A., Lelieveld, J., and Mohan, M. (2004). “Emission estimates and trends (1990–2000) for megacity Delhi and implications.” Atmos. Environ., 38(33), 5663–5681.
Guttikunda, S. K., and Calori, G. (2013). “A GIS based emissions inventory at spatial resolution for air pollution analysis in Delhi, India.” Atmos. Environ., 67, 101–111.
Hagan, M. T., and Menhaj, M. B. (1994). “Training feed forward networks with the Marquardt algorithm.” IEEE Trans. Neural Networks, 5(6), 989–993.
Hall, P., and Miller, H. (2009). “Using generalized correlation to effect variable selection in very high dimensional problems.” J. Comput. Graph. Stat., 18(3), 533–550.
Hornik, K., Stinchcombe, M., and White, H. (1989). “Multi layer feed forward networks are universal approximators.” Neural Networks, 2(5), 359–366.
Hsu, K.-L., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
Jain, A., and Srinivasulu, S. (2004). “Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques.” Water Resour. Res., 40(4), W04302.
Kukkonen, J., et al. (2003). “Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki.” Atmos. Environ., 37(32), 4539–4550.
Longhurst, J. W. S., Lindley, S. J., Watson, A. F. R., and Conlan, D. E. (1996). “The introduction of local air quality management in the United Kingdom: A review and theoretical framework.” Atmos. Environ., 30(23), 3975–3985.
MATLAB ANN Toolbox [Computer software]. Natick, MA, MathWorks.
Milionis, A. E., and Davis, T. D. (1994). “Regression and stochastic models for air pollution. Part I: Review comments and suggestions.” Atmos. Environ., 28(17), 2801–2810.
MOEF (Ministry of Environment and Forests). (1997). “White paper on air pollution on Delhi with an action plan.” Delhi, India.
Moseholm, L., Silva, J., and Larson, T. C. (1996). “Forecasting carbon monoxide concentration near a sheltered intersections using video traffic surveillance and neural networks.” Transp. Res., D1(1), 15–28.
Nagendra, S. M. S., and Khare, M. (2002). “Line source emission modelling: Review.” Atmos. Environ., 36(13), 2083–2098.
Nagendra, S. M. S., and Khare, M. (2004). “Artificial neural network based line source models for vehicular exhaust emission predictions of an urban roadway.” J. Transp. Res. D Transp. Environ., 9(3), 199–208.
Nagendra, S. M. S., and Khare, M. (2006). “Artificial neural network approach for modeling nitrogen dioxide dispersion from vehicular exhaust emissions.” Ecol. Modell., 190(1–2), 99–115.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models: 1. A discussion of principles.” J. Hydrol., 10(3), 282–290.
Pandey, J. S., Rakesh, K., and Devotta, S. (2005). “Health Risks of NO2, SPM and SO2 in Delhi (India).” Atmos. Environ., 39(36), 6868–6874.
Perez, P., and Trier, A. (2001). “Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile.” Atmos. Environ., 35(10), 1783–1789.
Pires, J. C. M., et al. (2012). “Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting.” Environ. Sci. Pollut. Res., 19(8), 3228–3234.
Quinlan, J. R. (1992). “Learning with continuous classes.” Proc., 5th Australian Joint Conf. on Artificial Intelligence, World Scientific, Singapore, 343–348.
Rege, M. A., and Tock, R. W. (1996). “A simple neural network for estimating emission rates of hydrogen sulphide and ammonia from single point source.” J. Air Waste Manage. Assoc., 46(10), 953–962.
Schnelle, K. B., and Dey, P. R. (2000). Atmospheric dispersion modelling compliance guide, McGraw Hill, New York.
Senthilkumar, A. R., Ojha, C. S. P., Goyal, M., Singh, R. D., and Swamee, P. K. (2012). “Modelling of suspended sediment concentration at kasol in India using ANN, fuzzy logic and decision tree algorithms.” J. Hydrol. Eng., 394-404.
Sharma, P. (1998). “Air quality modelling for an urban road intersection of Delhi city.” Ph.D. dissertation, Civil Engineering Dept., IIT Delhi, India.
Shi, J. P., and Harrison, R. M. (1997). “Regression modelling of hourly and concentration in urban air in London.” Atmos. Environ., 31(24), 4081–4094.
Simpson, P. K. (1989). “Foundations of neural networks.” Artificial neural networks: Paradigms, application and hardware implementations, E. S. Sinencio and C. Lau, eds., IEEE Press, New York, 3–24.
Srivastava, K., and Jain, V. K. (2008). “A study to characterize the influence of outdoor SPM and metals on indoor environments in Delhi.” J. Environ. Sci. Eng., 47(3), 222–231.
Varshney, K., and Padhy, P. K. (1998). “Total volatile organic compounds in urban environment of Delhi.” J. Air Waste Manage. Assoc., 48(5), 448–453.
Viotti, P., Liuti, G., and Genova, P. D. (2002). “Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia.” Ecol. Modell., 148(1), 27–46.
Witten, I. H., and Frank, E. (2005). Data mining: Practical machine learning tools and techniques, 2nd Ed., Morgan Kaufmann, San Francisco.
Ziomass, I. C., Melas, D., Zerefos, C. S., and Bais, A. F. (1995). “Forecasting peak pollutant levels from meteorological variables.” Atmos. Environ., 29(24), 3703–3711.
Zurada, J. M. (1997). Introduction to artificial neural systems, West Publishing, Mumbai, India.
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Journal of Hazardous, Toxic, and Radioactive Waste
Volume 20 • Issue 4 • October 2016
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© 2015 American Society of Civil Engineers.
History
Received: Jul 25, 2014
Accepted: Dec 22, 2014
Published online: Feb 24, 2015
Discussion open until: Jul 24, 2015
Published in print: Oct 1, 2016
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