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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Authors
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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Abstract
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.
Keywords
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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Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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