메뉴 건너뛰기




Volumn 110, Issue , 2017, Pages 71-76

Forecasting powder dispersion in a complex environment using Artificial Neural Networks

Author keywords

Artificial Neural Networks; Atmospheric dispersion modeling; Dust forecasting

Indexed keywords

AIR QUALITY; ATMOSPHERIC MOVEMENTS; COMPLEX NETWORKS; DUST; METEOROLOGY; QUALITY CONTROL; URBAN GROWTH; WEATHER FORECASTING;

EID: 85014265843     PISSN: 09575820     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.psep.2017.02.003     Document Type: Article
Times cited : (15)

References (19)
  • 1
    • 0027599793 scopus 로고
    • Universal approximation bounds for superpositions of a sigmoidal function
    • Barron, A.R., Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inf. Theory 39 (1993), 930–945, 10.1109/18.256500.
    • (1993) IEEE Trans. Inf. Theory , vol.39 , pp. 930-945
    • Barron, A.R.1
  • 2
    • 0027610496 scopus 로고
    • 2 concentrations in highly polluted industrial areas of complex terrain
    • Boznar, M., Lesjak, M., Mlakar, P., A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmos. Environ. B. Urban Atmos. 27 (1993), 221–230, 10.1016/0957-1272(93)90007-S.
    • (1993) Atmos. Environ. B. Urban Atmos. , vol.27 , pp. 221-230
    • Boznar, M.1    Lesjak, M.2    Mlakar, P.3
  • 4
    • 79955521242 scopus 로고    scopus 로고
    • Dispersion Coefficients for Gaussian Puff Models
    • Cao, X., Roy, G., Hurley, W.J., Andrews, W.S., Dispersion Coefficients for Gaussian Puff Models. Boundary-Layer Meteorol. 139:3 (2011), 487–500, 10.1007/s10546-011-9595-3.
    • (2011) Boundary-Layer Meteorol. , vol.139 , Issue.3 , pp. 487-500
    • Cao, X.1    Roy, G.2    Hurley, W.J.3    Andrews, W.S.4
  • 5
    • 84949502876 scopus 로고    scopus 로고
    • Numerical modelling for wind farm operational assessment in complex terrain
    • 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.
    • (2015) J. Wind Eng. Ind. Aerodyn. , vol.147 , pp. 320-329
    • Castellani, F.1    Astolfi, D.2    Burlando, M.3    Terzi, L.4
  • 6
    • 3342987585 scopus 로고    scopus 로고
    • Air quality model performance evaluation
    • Chang, J.C., Hanna, S.R., Air quality model performance evaluation. Meteorol. Atmos. Phys. 87 (2004), 167–196, 10.1007/s00703-003-0070-7.
    • (2004) Meteorol. Atmos. Phys. , vol.87 , pp. 167-196
    • Chang, J.C.1    Hanna, S.R.2
  • 7
    • 0028543366 scopus 로고
    • Training feedforward networks with the Marquardt algorithm
    • Hagan, M.T., Menhaj, M.B., Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5 (1994), 989–993.
    • (1994) IEEE Trans. Neural Netw. , vol.5 , pp. 989-993
    • Hagan, M.T.1    Menhaj, M.B.2
  • 8
    • 84860464129 scopus 로고    scopus 로고
    • Acceptance criteria for urban dispersion model evaluation
    • Hanna, S., Chang, J., Acceptance criteria for urban dispersion model evaluation. Meteorol. Atmos. Phys. 116 (2012), 133–146, 10.1007/s00703-011-0177-1.
    • (2012) Meteorol. Atmos. Phys. , vol.116 , pp. 133-146
    • Hanna, S.1    Chang, J.2
  • 9
    • 5044221704 scopus 로고    scopus 로고
    • Evaluation of the ADMS, AERMOD, and ISC3 dispersion models with the optex, duke forest, kincaid, indianapolis, and lovett field data sets
    • 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.
    • (1999) Int. J. Environ. Pollut. , vol.3
    • Hanna, S.R.1    Egan, B.A.2    Purdum, J.3    Wagler, J.4
  • 10
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators. Neural Netw 2 (1989), 359–366.
    • (1989) Neural Netw , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 12
    • 84859101501 scopus 로고    scopus 로고
    • Optimization of the generalization capability for rainfall-runoff modeling by neural networks: the case of the Lez aquifer (southern France)
    • 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.
    • (2012) Environ. Earth Sci. , vol.65 , pp. 2365-2375
    • Kong A Siou, L.1    Johannet, A.2    Valérie, B.E.3    Pistre, S.4
  • 14
    • 84983516374 scopus 로고    scopus 로고
    • Atmospheric dispersion modeling using Artificial Neural Network based cellular automata
    • Lauret, P., Heymes, F., Aprin, L., Johannet, A., Atmospheric dispersion modeling using Artificial Neural Network based cellular automata. Environ. Model. Softw. 85 (2016), 56–69, 10.1016/j.envsoft.2016.08.001.
    • (2016) Environ. Model. Softw. , vol.85 , pp. 56-69
    • Lauret, P.1    Heymes, F.2    Aprin, L.3    Johannet, A.4
  • 15
    • 0036134847 scopus 로고    scopus 로고
    • Application of artificial neural networks to modeling the transport and dispersion of tracers in complex terrain
    • 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.
    • (2002) Atmos. Environ. , vol.36 , pp. 561-570
    • Podnar, D.1    Koračin, D.2    Panorska, A.3
  • 17
    • 77957817663 scopus 로고    scopus 로고
    • Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks
    • Stavrakakis, G.M., Karadimou, D.P., Zervas, P.L., Sarimveis, H., Markatos, N.C., Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks. Build. Environ. 46 (2011), 298–314, 10.1016/j.buildenv.2010.07.021.
    • (2011) Build. Environ. , vol.46 , pp. 298-314
    • Stavrakakis, G.M.1    Karadimou, D.P.2    Zervas, P.L.3    Sarimveis, H.4    Markatos, N.C.5
  • 18
    • 84983510869 scopus 로고    scopus 로고
    • Modélisation de la dispersion atmosphérique en présence d'obstacles complexes: application à l’étude de sites industriels
    • Ecole Centrale de Lyon
    • Vendel, F., Modélisation de la dispersion atmosphérique en présence d'obstacles complexes: application à l’étude de sites industriels. 2011, Ecole Centrale de Lyon.
    • (2011)
    • Vendel, F.1
  • 19
    • 84902315848 scopus 로고    scopus 로고
    • Ambient (outdoor) air quality and health [WWW Document]. Fact sheet
    • WHO Media centre, Ambient (outdoor) air quality and health [WWW Document]. Fact sheet. 2016 http://www.who.int/mediacentre/factsheets/fs313/en/.
    • (2016)
    • WHO Media centre1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.