Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data.

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ID: 94331
2019
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Abstract
Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO and CO.
Reference Key
ly2019developmentsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ly, Hai-Bang;Le, Lu Minh;Phi, Luong Van;Phan, Viet-Hung;Tran, Van Quan;Pham, Binh Thai;Le, Tien-Thinh;Derrible, Sybil;
Journal sensors
Year 2019
DOI
E4941
URL
Keywords
air quality particle swarm optimization simulated annealing adaptive neuro-fuzzy inference system

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