Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms

References

• Ahangar-Asr, A., A. Johari, and A. A. Javadi. 2012. An evolutionary approach to modelling the soil–water characteristic curve in unsaturated soils. Computers and Geosciences 43:25–33. doi:10.1016/j.cageo.2012.02.021.
   Web of Science ®Google Scholar
• Allen, C. T., S. E. Haupt, and G. S. Young. 2007a. Source characterization with a genetic algorithm-coupled dispersion-backward model incorporating scipuff. Journal of Applied Meteorology and Climatology 46 (3):273–87. doi:10.1175/JAM2459.1.
   Web of Science ®Google Scholar
• Allen, C. T., G. S. Young, and S. E. Haupt. 2007b. Improving pollutant source characterization by better estimating wind direction with a genetic algorithm. Atmospheric Environment 41 (11):2283–89. doi:10.1016/j.atmosenv.2006.11.007.
   Web of Science ®Google Scholar
• Biltoft, C. A. 1998. Dipole pride 26: Phase ii of defense special weapons agency transport and dispersion model validation. Technical report, DTIC Document, Defense Technical Information Center.
   Google Scholar
• Cervone, G., and P. Franzese. 2010a. Machine learning for the source detection of atmospheric emissions. Proceedings of the 8th Conference on Artificial Intelligence Applications to Environmental Science, Number J1.7, January.
   Google Scholar
• Cervone, G., and P. Franzese. 2010b. Monte Carlo source detection of atmospheric emissions and error functions analysis. Computers and Geosciences 36 (7):902–09. doi:10.1016/j.cageo.2010.01.007.
   Web of Science ®Google Scholar
• Cervone, G., and P. Franzese. 2011. Non-darwinian evolution for the source detection of atmospheric releases. Atmospheric Environment 45 (26):4497–506. doi:10.1016/j.atmosenv.2011.04.054.
   Web of Science ®Google Scholar
• Cervone, G., P. Franzese, and A. Grajdeanu. 2010a. Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms. Atmospheric Environment 44:3787–96. doi:10.1016/j.atmosenv.2010.06.046.
   Web of Science ®Google Scholar
• Cervone, G., P. Franzese, and A. P. Keesee. 2010b. Algorithm quasi-optimal (AQ) learning. Wires: Computational Statistics 2 (2):218–36.
   Google Scholar
• Chang, J. C., P. Franzese, K. Chayantrakom, and S. R. Hanna. 2003a. Evaluations of calpuff, hpac, and vlstrack with two mesoscale field datasets. Journal of Applied Meteorology 42 (4):453–66. doi:10.1175/1520-0450(2003)042<0453:EOCHAV>2.0.CO;2.
   Google Scholar
• Chang, J. C., P. Franzese, K. Chayantrakom, and S. R. Hanna. 2003b. Evaluations of CALPUFF, HPAC, and VLSTRACK with two mesoscale field datasets. Journal of Applied Meteorology 42 (4):453–66. doi:10.1175/1520-0450(2003)042<0453:EOCHAV>2.0.CO;2.
   Google Scholar
• Chang, J. C., and S. R. Hanna. 2004. Air quality model performance evaluation. Meteorology and Atmospheric Physics 87:167–96. doi:10.1007/s00703-003-0070-7.
   Web of Science ®Google Scholar
• Chang, J. C., S. R. Hanna, Z. Boybeyi, and P. Franzese. 2005. Use of Salt Lake City Urban 2000 field data to evaluate the Urban Hazard Prediction Assessment Capability (HPAC) dispersion model. Journal of Applied Meteorology 44 (4):485–501. doi:10.1175/JAM2205.1.
   Google Scholar
• Chow, F., B. Kosovic´, and T. Chan. 2006. Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations. Proceedings of the 86th American Meteorological Society Annual Meeting, Atlanta, GA, 12–22.
   Google Scholar
• De Jong, K. 2008. Evolutionary computation: A unified approach. Proceedings of the 2008 GECCO Conference on Genetic and Evolutionary Computation, ACM, New York, NY, USA, 2245–58.
   Google Scholar
• Delle Monache, L., J. Lundquist, B. Kosovic´, G. Johannesson, K. Dyer, R. Aines, F. Chow, R. Belles, W. Hanley, S. Larsen, G. Loosmore, J. Nitao, G. Sugiyama, and P. Vogt. 2008. Bayesian inference and markov chain monte carlo sampling to reconstruct a contaminant source on a continental scale. Journal of Applied Meteorology and Climatology 47:2600–13. doi:10.1175/2008JAMC1766.1.
   Web of Science ®Google Scholar
• Franzese, P. 2003. Lagrangian stochastic modelling of a fluctuating plume in the convective boundary layer. Atmospheric Environment 37:1691–701. doi:10.1016/S1352-2310(03)00003-7.
   Web of Science ®Google Scholar
• Gelman, A., J. Carlin, H. Stern, and D. Rubin. 2003. Bayesian data analysis, 668. Chapman & Hall/CRC: London, UK.
   Google Scholar
• Hanna, S. R., J. C. Chang, and G. D. Strimaitis. 1993. Hazardous gas model evaluation with field observations. Atmospheric Environment 27A:2265–85. doi:10.1016/0960-1686(93)90397-H.
   Web of Science ®Google Scholar
• Haupt, S. E. 2005. A demonstration of coupled receptor/dispersion modeling with a genetic algorithm. Atmospheric Environment 39 (37):7181–89, December. doi:10.1016/j.atmosenv.2005.08.027.
   Web of Science ®Google Scholar
• Haupt, S. E., G. S. Young, and C. T. Allen. 2007. A genetic algorithm method to assimilate sensor data for a toxic contaminant release. Journal of Computers 2 (6):85–93, August. doi:10.4304/jcp.2.6.85-93.
   Google Scholar
• Holland, J. 1975. Adaptation in natural and artificial systems. Cambridge, MA: The MIT Press.
   Google Scholar
• Johannesson, G., B. Hanley, and J. Nitao. 2004. Dynamic Bayesian Models via Monte Carlo - An introduction with examples. Technical Report UCRL-TR-207173, Lawrence Livermore National Laboratory, October.
   Google Scholar
• Krysta, M., M. Bocquet, B. Sportisse, and O. Isnard. 2006. Data assimilation for short-range dispersion of radionuclides: An application to wind tunnel data. Atmospheric Environment 40 (38):7267–79. doi:10.1016/j.atmosenv.2006.06.043.
   Web of Science ®Google Scholar
• Lattner, A. D., and G. Cervone. 2012. Ensemble modeling of transport and dispersion simulations guided by machine learning hypotheses generation. Computers and Geosciences 48:267–79. doi:10.1016/j.cageo.2012.01.017.
   Web of Science ®Google Scholar
• Long, K. J., S. E. Haupt, and G. S. Young. 2010. Assessing sensitivity of source term estimation. Atmospheric Environment 44 (12):1558–67. doi:10.1016/j.atmosenv.2010.01.003.
   Web of Science ®Google Scholar
• Senocak, I., N. Hengartner, M. Short, and W. Daniel. 2008. Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference. Atmospheric Environment 42 (33):7718–27. doi:10.1016/j.atmosenv.2008.05.024.
   Web of Science ®Google Scholar
• Sykes, R. I., and R. S. Gabruk. 1997. A second-order closure model for the effect of averaging time on turbulent plume dispersion. Journal of Applied Meteorology 36:165–84. doi:10.1175/1520-0450(1997)036<1038:ASOCMF>2.0.CO;2.
   Google Scholar
• Sykes, R. I., W. S. Lewellen, and S. F. Parker. 1984. A turbulent transport model for concentration fluctuation and fluxes. Journal Fluid Mechanisms 139:193–218. doi:10.1017/S002211208400032X.
   Web of Science ®Google Scholar
• Tiampo, K., J. Ferna´Ndez, G. Jentzsch, M. Charco, and J. Rundle. 2004. Volcanic source inversion using a genetic algorithm and an elastic-gravitational layered earth model for magmatic intrusions. Computers and Geosciences 30 (9):985–1001. doi:10.1016/j.cageo.2004.07.005.
   Web of Science ®Google Scholar
• Watson, T., R. Keislar, B. Reese, D. George, and C. Biltoft. 1998. The defense special weapons agency dipole pride 26 field experiment. NOAA Air Resources Laboratory Tech. Memo. ERL ARL-225.
   Google Scholar