Prediction of dust concentration in open cast coal mine using artificial neural network

Atmospheric Pollution Research

Volumn 3, Issue 2, 2012, Pages 211-218

  Lal, Bindhu ^a   Tripathy, Sanjaya Shankar ^a  

^a BIRLA INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Dispersion coefficients; Dust concentration; Feed forward neural network; Model performance; Wind speed

Indexed keywords

EID: 84882794457     PISSN: 13091042     EISSN: None     Source Type: Journal    
DOI: 10.5094/APR.2012.023     Document Type: Article

References (26)

1. Alekseev, K.P.G., Seixas, J.M., 2002. Forecasting the air transport demand for passengers with neural modelling. Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN), November 11-14, 2002, Pernambuco, Brazil, IEEE 86-91.
2. Baxter, C.W., Stanley, S.J., Zhang, Q., Smith, D.W., 2002. Developing artificial neural network models of water treatment processes: a guide for utilities. Journal of Environmental Engineering Science 1, 201-211.
3. Binkowski, F.S., Ching, J.K.S., 1996. Modelling fine particulate mass and visibility using the EPA regional particulate models. Ninth Joint Conference on Applications of Air Pollution Meteorology with the A and WMA, January 28-February 2, 1996, Boston, 565-569.
4. Bose, N. K, Liang, P., 1998. Neural network fundamental with graphs, algorithm and applications, Tata McGraw-Hill, New Delhi.
5. Chaulya, S.K., Ahmad, M., Singh, R.S., Bandopadhyay, L.K., Bondyopadhay, C., Mondal, G.C., 2003. Validation of two air quality models for Indian mining conditions. Environmental Monitoring and Assessment 82, 23-43.
6. Chaulya, S.K., Chakraborty, M.K., Ahmad, M., Singh, R. S, Bondyopadhay, C., Mondal, G.C., Pal, D., 2002. Development of empirical formulate to determine emission rate from various opencast coal mining operations. Water, Air and Soil Pollution 140, 21-55.
7. Chelani, A.B., Gajghate, D.G., Hasan, M.Z., 2002. Prediction of ambient PM10 and toxic metals using artificial neural networks. Journal of the Air and Waste Management Association 52, 805-810.
8. Cohn, R.D., Eder, B.K., Leduc., S.K., Dennis, R.L., 2001. Development of an aggregation and episode selection scheme to support the models-3 community multiscale air quality model. Journal of Applied Meteorology 40, 210-228.
9. Dastoor, A.P., Pudykiewicz, J., 1996. A numerical global meteorological sulfur transport model and its application to arctic air pollution. Atmospheric Environment 30, 1501-1513.
10. Ghose, M.K., 2002. Air Pollution due to opencast coal mining and the characteristics of air borne dust-an Indian scenario. Environmental Studies 59, 211-228.
11. Juang, H.C., Chen, C.J., 1999. CPT-Based liquefaction evaluation using artificial neural networks. Computer-Aided Civil and Infrastructure Engineering 14, 221-229.
12. Kumar, A., Dixit, S., Varadarajan, C., Vijayan, A., Masuraha, A., 2006. Evaluation of the AERMOD dispersion model as a function of atmospheric stability for an urban area. Environmental Progress 25, 141-151.
13. 2.5) forecasting. Journal of the Air Waste Management Association 52, 1096-1101.
14. Nagendra, S.M.S., Khare, M., 2005. Modelling urban air quality using artificial neural network. Clean Technologies and Environmental Policy 7, 116-126.
15. st International IEEE Symposium on Intelligent Systems, September 10-12, 2022, Varna, Bulgaria, 283-288.
16. Patel, V.C., Kumar, A., 1998. Evaluation of three air dispersion models: ISCST2, ISCLT2 and SCREEN2 for mercury emissions in an urban area. Environmental Monitoring and Assessment 53, 259-277.
17. Popescu, I., Naforni, I., Gavriloaia, G., Constantinou, P., Gordan, C., 2001. Field strength prediction in indoor environment with a neural model. Facta Universitatis Series: Electronics and Energetics 14,329-336.
18. Reeve, R., Hallam, J., 2005. An analysis of neural models for walking control. IEEE Transactions on Neural Networks 16, 733-742.
19. Riswadkar, R.M., Kumar. A., 1994. Evaluation of the industrial source complex short-term model in a large-scale multiple source region for different stability classes. Environmental Monitoring and Assessment 33, 19-32.
20. Rogers, L.L., Dowla, F.U., 1994. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resources Research 30, 457-481.
21. Roy, S., Adhikari, G.R., Renaldy, T.A., Jha, A.K., 2011. Development of multiple regression and neural network models for assessment of blasting dust at a large surface coal mine. Journal of Environmental Science and Technology 4, 284-301.
22. Saltbones, J., Foss, A., Bartnicki, J., 1998. Norwegian meteorological institute's real-time dispersion model snap (severe nuclear accident program): runs for ETEX and ATMES II experiments with different meteorological input. Atmospheric Environment 32, 4277-4283.
23. 2 and PM concentrations in a coastal mining area (Zonguldak, Turkey) using an artificial neural network. Polish Journal of Environmental Studies 16, 633-638.
24. Vardoulakis, S., Fisher, B.E.A., Pericleous, K., Gonzalez-Flesca, N., 2003. Modelling air quality in street canyons: a review. Atmospheric Environment 37, 155-182.
25. Viotti, P., Liuti, G., Genova, P.D., 2002. Atmospheric urban pollution: application of an artificial neural network (ANN) to the city of Perugia. Ecological Modeling 148, 27-46.
26. Willmot, C.J., 1982. Some comments on the evaluation of model performances. Bulletin of the American Meteorological Society 63, 1309-1313.