Tên bài báo:

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data
Tác giả:
Lý Hải Bằng
Tham gia cùng:
Phạm Thái Bình
Phí Lương Vân
Trần Văn Quân
Tạp chí:
Năm xuất bản:
Từ trang 1 đến trang 17
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

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 NO2 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 NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO

Từ khóa:

Air quality Adaptive Neuro-Fuzzy Inference System Particle Swarm Optimization Simulated Annealing
Thông tin tác giả
Lý Hải Bằng

Lý Hải Bằng

Tiến sĩ

Lý lịch khoa học