RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration
American Journal of Biological and Environmental Statistics
Volume 4, Issue 2, June 2018, Pages: 66-73
Received: Jun. 26, 2018; Accepted: Jul. 16, 2018; Published: Aug. 9, 2018
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Zheng Xipeng, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China
Yang Shunsheng, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China
Xiang Wenchuan, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China
Chen Yu, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
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The arrangement of the sensors in the air pollutant distribution space was designed by segmented array. A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors in order to reduce the sensor arrangement cost. According to the measured values and the predicted data, Gaussian plume diffusion model for air pollution was created, and the quadratic optimization model and inversion method for inverse calculation of single pollution source and multi pollution source were built. Single pollution source and double pollution source was inversely optimized by three different intelligent optimized algorithms in experimental simulation in order to obtain the accurate information on pollution sources. The validity of this method was verified so as to provide a reference for subsequent research.
Air Pollution, Sensor, Gaussian Plume Diffusion Model, Intelligent Optimized Algorithm
To cite this article
Zheng Xipeng, Yang Shunsheng, Xiang Wenchuan, Chen Yu, RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration, American Journal of Biological and Environmental Statistics. Vol. 4, No. 2, 2018, pp. 66-73. doi: 10.11648/j.ajbes.20180402.13
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Turner D B. Atmospheric dispersion modeling. a critical review [J]. J. Air Pollut. Control Assoc. (United States), 1979, 29 (5).
Lushi E, Stockie J M. An inverse Gaussian plume approach for estimating atmospheric pollutant emissions from multiple point sources [J]. Atmospheric Environment, 2010, 44 (8): 1097-1107.
Wu Zengmao, Sheng Lifang, Liu Feng. Research progress of numerical prediction methods for urban regional atmospheric quality [J]. Meteorological Technology, (In China) 1999, 1:11-15.
Liley J B. Analytic solution of a one-dimensional equation for aerosol and gas dispersion in the stratosphere [J]. Journal of the atmospheric sciences, 1995, 52 (18): 3283-3288.
Lin J S, Hildemann L M. A generalized mathematical scheme to analytically solve the atmospheric diffusion equation with dry deposition [J]. Atmospheric Environment, 1997, 31 (1): 59-71.
Annunzio A J, Young G S, Haupt S E. A Multi-Entity Field Approximation to determine the source location of multiple atmospheric contaminant releases [J]. Atmospheric environment, 2012, 62: 593-604.
Senocak I, Hengartner N W, Short M B, et al. Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference [J]. Atmospheric Environment, 2008, 42 (33): 7718-7727.
Annunzio A J, Young G S, Haupt S E. Utilizing state estimation to determine the source location for a contaminant [J]. Atmospheric environment, 2012, 46: 580-589.
Chen J M, Xu D H, Zhu R. Application of Genet ic Algorithms to Point-source Inversion [J]. Meteorological, 2002, 28 (9): 12-16.
Zhang J F, Jiang C, Wang Z, et al. PSO Algorithm for Back-calculation of Source Intensity [J]. China Safety Science Journal (CSSJ), 2010, 10: 024.
Mulholland M, Seinfeld J H. Inverse air pollution modelling of urban-scale carbon monoxide emissions [J]. Atmospheric Environment, 1995, 29 (4): 497-516.
Seibert P, Frank A. Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode [J]. Atmospheric Chemistry and Physics, 2004, 4 (1): 51-63.
Shen X Y, Bi Z H, Liu H F. Gas Concentration Retrieval Algorithm Based on Kalman Filtering Theory [J]. Opto-Electronic Engineering, 2008, 12: 015.
Enting I G, Newsam G N. Inverse problems in atmospheric constituent studies: II. Sources in the free atmosphere [J]. Inverse Problems, 1990, 6 (3): 349.
Goyal A, Small M J, von Stackelberg K, et al. Estimation of fugitive lead emission rates from secondary lead facilities using hierarchical Bayesian models [J]. Environmental science & technology, 2005, 39 (13): 4929-4937.
Johannesson G, Hanley B, Nitao J. Dynamic bayesian models via monte carlo-an introduction with examples [J]. Lawrence Livermore National Laboratory, UCRL-TR-207173, 2004.
Senocak I. Application of a Bayesian inference method to reconstruct short-range atmospheric dispersion events [C]//AIP Conference Proceedings. 2010.
Feng F Wang Z F, Tang X. 2016. Development of an adaptive algorithm based on the shooting method and its application in the problem of estimating air pollutant emissions [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 40 (4): 719−729.
Xu B L, L H, H Y, et al. Design and Implement a Parallel Algorithm of Gauss Plume Model for Air Pollution Dispersion [J]. Transactions of Beijing Institute of Technology, 2014, 34 (11): 1145-1149.
Ferragut L, Asensio M I, Cascón J M, et al. An efficient algorithm for solving a multi-layer convection–diffusion problem applied to air pollution problems [J]. Advances in Engineering Software, 2013, 65: 191-199.
Liu J. Radial Basis Function (RBF) neural network control for mechanical systems: design, analysis and Matlab simulation [M]. Springer Science & Business Media, 2013.
Hartman E J, Keeler J D, Kowalski J M. Layered neural networks with Gaussian hidden units as universal approximations [J]. Neural computation, 1990, 2 (2): 210-215.
Park J, Sandberg I W. Universal approximation using radial-basis-function networks [J]. Neural computation, 1991, 3 (2): 246-257.
Jiang W M, Cao W J, Jiang R B. Air pollution meteorology tutorial [M]. Meteorological Press., 1993: 77-86.
Zheng X P, Chen Z Q. Back Calculation of Source Strength and Location of Toxic Gases Releasing Based on Pattern Search Method [J]. Zhongguo Anquan Kexue Xuebao, 2010, 20 (5): 29-34.
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