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.
Highlights
In recent years, the increasing vehicles and factories and mines consume a lot of fossil energy, resulting in increasing air pollutant emissions, and more and more serious air pollution [1]
Based on the pollutant data acquired by the sensors in segmented array, RBF neural network and different algorithms were used to study the air pollution diffusion inversion, and mainly simulate the intensity and position of single pollution source and multi pollution source in this paper
The followings were concluded: (1) The pollutant concentrations predicted by RBF neural network had smaller absolute error and relative error than those by BP neural network algorithm; the RBF neural network model could be used to study the air pollution diffusion
Summary
The increasing vehicles and factories and mines consume a lot of fossil energy, resulting in increasing air pollutant emissions, and more and more serious air pollution [1]. As two main factors of influencing the pollutant diffusion, pollution source position and intensity have been experimentally and simulatively studied by different researchers. The neural network model has been used for inverse calculation of air pollutants more often because of its advantages, including low resource consumption, more acquired data, etc., over the measured pollutant data. The current studies on neural network model-based accurate calculation of pollutants still are facing many difficulties [3]. Different researchers’ inverse calculation of pollutant source concentrations and positions by the pollutant concentration data and meteorological condition measured by the sensors in the space showed that inverse calculation could be achieved on the premise of enough pollutant concentration distribution data [4,5,6]. The method of solving the problems about inverse calculation of pollution sources mainly include: (1) Gaussian plume diffusion model-based inverse calculation of pollution sources by intelligent optimized algorithm, which always applies to pollutant
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