Atmospheric dispersion modeling using Artificial Neural Network based cellular automata

Environmental Modelling and Software

Volumn 85, Issue , 2016, Pages 56-69

  Lauret, Pierre ^a   Heymes, Frédéric ^a   Aprin, Laurent ^a   Johannet, Anne ^a  

^a ECOLE DES MINES D ALÈS   (France)

Author keywords

Artificial Neural Network; Atmospheric dispersion; Cellular Automata; Computational fluid dynamics

Indexed keywords

ADVECTION; CELLULAR AUTOMATA; COMPLEX NETWORKS; COMPUTATIONAL FLUID DYNAMICS; DIFFUSION IN LIQUIDS; FLUID DYNAMICS; NEURAL NETWORKS; PHOTOLITHOGRAPHY;

ADVECTION-DIFFUSION EQUATION; ARCHITECTURE SELECTION; ATMOSPHERIC DISPERSION; ATMOSPHERIC DISPERSION MODELING; COEFFICIENT OF DETERMINATION; COMPLEX CONFIGURATION; NEURAL NETWORK DESIGNS; SAMPLING SELECTIONS;

ATMOSPHERIC MOVEMENTS;

ACCURACY ASSESSMENT; ADVECTION-DIFFUSION EQUATION; ARTIFACT; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC MODELING; CELLULAR AUTOMATON; COMPUTATIONAL FLUID DYNAMICS; DATABASE; EMPIRICAL ANALYSIS; METHANE; NETWORK DESIGN;

EID: 84983516374     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2016.08.001     Document Type: Article

References (42)

1. Artigue, G., Johannet, A., Borrell, V., Pistre, S., Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France). Nat. Hazards Earth Syst. Sci. 12 (2012), 3307–3324, 10.5194/nhess-12-3307-2012.
2. Ak, R., Li, Y., Vitelli, V., Zio, E., A genetic algorithm and neural network technique for predicting wind power under uncertainty. Chem. Eng. Trans. 33 (2013), 925–930, 10.3303/CET1333155.
3. Almeida, C.M., Gleriani, J.M., Castejon, E.F., Soares-Filhow, B.S., Using neural networks and cellular automata for modeling intra-urban land use dynamics. Int. J. Geogr. Inf. Sci. 22 (2008), 943–963.
4. Ansys Inc, ANSYS Fluent 14.0 User's Guide. 2011.
5. Balzter, H., Braun, P.W., Köhler, W., Cellular automata models for vegetation dynamics. Ecol. Modell. 107 (1998), 113–125, 10.1016/S0304-3800(97)00202-0.
6. Barad, M.L., Project prairie grass, a field program in diffusion. Geophys. Res. Pap., 1, 1958, 299.
7. Barron, A.R., Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inf. Theory 39 (1993), 930–945, 10.1109/18.256500.
8. Brambilla, S., Totaro, R., Manca, D., Simulation of the LPG release, dispersion, and explosion in the Viareggio railway accident. Chem. Eng. Trans. 19 (2010), 195–200, 10.3303/CET1019032.
9. Cao, X., Modelling the Concentration Distribution of Non-buoyant Aerosols Released from Transient Point Sources into the Atmosphere. 2007, Queen's University, Ontario, Canada.
10. Caputo, M., Giménez, M., Schlamp, M., Intercomparison of atmospheric dispersion models. Atmos. Environ. 37 (2003), 2435–2449, 10.1016/S1352-2310(03)00201-2.
11. Castellani, F., Astolfi, D., Burlando, M., Terzi, L., Numerical modelling for wind farm operational assessment in complex terrain. J. Wind Eng. Ind. Aerodyn. 147 (2015), 320–329, 10.1016/j.jweia.2015.07.016.
12. Chang, J.C., Hanna, S.R., Air quality model performance evaluation. Meteorol. Atmos. Phys. 87 (2004), 167–196, 10.1007/s00703-003-0070-7.
13. Chopard, B., Dupuis, A., Masselot, A., Luthi, P., Cellular automata and lattice boltzmann techniques: an approach to model and simulate complex systems. Adv. Complex Syst. 5 (2002), 1–144.
14. Dreyfus, G., Neural Networks. 2005, Springer-Verlag, Berlin/Heidelberg, 10.1007/3-540-28847-3.
15. Elmenreich, W., Fehérvári, I., Evolving self-organizing cellular automata based on neural network genotypes. Proc. 5th Int. Conf. Self-organizing Syst, 2011, 16–25, 10.1007/978-3-642-19167-1_2.
16. Geman, S., Doursat, R., Bienenstock, E., Neural networks and the bias/variance dilemma. Neural Comput. 4 (1992), 1–58, 10.1162/neco.1992.4.1.1.
17. Guariso, G., Maniezzo, V., Air quality simulation through cellular automata. Environ. Softw. 7 (1992), 131–141.
18. Hagan, M.T., Menhaj, M.B., Training feedforward networks with the marquardt algorithm. IEEE Trans. Neural Netw. 5 (1994), 989–993.
19. Hanna, S.R., Egan, B.A., Purdum, J., Wagler, J., Evaluation of the ADMS, AERMOD, and ISC3 dispersion models with the optex, duke forest, kincaid, indianapolis, and lovett field data sets. Int. J. Environ. Pollut., 3, 1999.
20. Hardy, J., Pomeau, Y., De Pazzis, O., Time evolution of two-dimensional model system. I. Invariant states and time correlation functions. J. Math. Phys. 14 (1973), 1746–1759.
21. Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators. Neural Netw. 2 (1989), 359–366.
22. Itami, M., Simulating spatial dynamics: cellular automata theory. Landsc. Urban Plan. 30 (1994), 27–47.
23. Kong A Siou, L., Johannet, A., Borrell, V., Pistre, S., Complexity selection of a neural network model for karst flood forecasting: the case of the Lez Basin (southern France). J. Hydrol. 403 (2011), 367–380, 10.1016/j.jhydrol.2011.04.015.
24. Kong A Siou, L., Johannet, A., Valérie, B.E., Pistre, S., Optimization of the generalization capability for rainfall–runoff modeling by neural networks: the case of the Lez aquifer (southern France). Environ. Earth Sci. 65 (2012), 2365–2375, 10.1007/s12665-011-1450-9.
25. Launder, B.E., Spalding, D.B., The numerical computation of turbulent flows. Comput. Methods Appl. Mech. Eng. 3 (1974), 269–289.
26. Lauret, P., Heymes, F., Aprin, L., Johannet, A., Munier, L., Lapébie, E., Near field atmospheric dispersion modelling on an industrial site using neural networks. Chem. Eng. Trans. 31 (2013), 151–156, 10.3303/CET1331026.
27. Lauret, P., Perrin, M., Heymes, F., Aprin, L., Slangen, P., Pey, A., Steinkrauss, M., Atmospheric Powder Dispersion in an Urban Area 48. 2016.
28. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., Backpropagation applied to handwritten Zip Code recognition. Neural Comput. 1 (1989), 541–551.
29. Li, X., Yeh, A.G.-O., Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int. J. Geogr. Inf. Sci. 16 (2002), 323–343, 10.1080/13658810210137004.
30. Marin, M., Rauch, V., Rojas-Molina, A., Lopez-Cajun, C.S., Herrera, A., Castano, V.M., Cellular automata simulation of dispersion of pollutants. Comput. Mater. Sci. 18 (2000), 132–140.
31. Nerrand, O., Roussel-Ragot, P., Personnaz, L., Dreyfus, G., Marcos, S., Neural networks and nonlinear adaptive filtering: unifying concepts and new algorithms. Neural Comput. 5 (1993), 165–199.
32. Pelliccioni, a., Gariazzo, C., Tirabassi, T., A neural net-air dispersion model validation study using the Indianapolis urban data set. Int. J. Environ. Pollut., 40, 2010, 70, 10.1504/IJEP.2010.030884.
33. Podnar, D., Koračin, D., Panorska, A., Application of artificial neural networks to modeling the transport and dispersion of tracers in complex terrain. Atmos. Environ. 36 (2002), 561–570, 10.1016/S1352-2310(01)00446-0.
34. Sarkar, C., Abbasi, S.A., Cellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries. J. Hazard. Mater 137 (2006), 8–30, 10.1016/j.jhazmat.2006.01.081.
35. Sharan, M., Gopalakrishnan, S.G., Bhopal gas accident: a numerical simulation of the gas dispersion event. Environ. Model. Softw. 12 (1997), 135–141, 10.1016/S1364-8152(96)00054-0.
36. Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.Y., Hjalmarsson, H., Juditsky, A., Nonlinear black-box modeling in system identification: unified overview. Automatica 31 (1995), 1691–1724.
37. Stone, M., Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B 36 (1974), 111–147.
38. Toukourou, M., Johannet, A., Dreyfus, G., Ayral, P.-A., Rainfall-runoff modeling of flash floods in the absence of rainfall forecasts: the case of “Cévenol flash floods. Appl. Intell. 35 (2011), 178–189, 10.1007/s10489-010-0210-y.
39. Vendel, F., Modélisation de la dispersion atmosphérique en présence d'obstacles complexes : application à l’étude de sites industriels. 2011, Ecole Centrale de Lyon.
40. Wolfram, S., Statistical mechanics of cellular automata. Rev. Mod. Phys. 55 (1983), 601–644.
41. Wu, F., Simulating Urban Encroachment on Rural Land with Fuzzy-logic-controlled Cellular Automata in a Geographical Information System, vol. 53, 1998, 293–308, 10.1006/jema.1998.0195.
42. Zio, E., Podofillini, L., Zille, V., A combination of Monte Carlo simulation and cellular automata for computing the availability of complex network systems. Reliab. Eng. Syst. Saf. 91 (2006), 181–190, 10.1016/j.ress.2004.12.002.