International Conference on Innovation in Artificial Intelligence (ICIAI 2021).
The spatial heterogeneity and temporal variability of air pollution in urban environments make air quality inference for fine-grained air pollution monitoring extremely challenging. Most of the existing work estimates the air quality using sparse measurements collected from a limited number of fixed monitoring stations. In this work, we propose a geographically context-aware random forest model for street-level air quality inference using high spatial resolution data collected by opportunistic mobile sensor network. Compared with traditional random forest model, the proposed method builds a local model for each location by considering the neighbors in both geographical and feature space. The model is evaluated on our real air quality dataset collected from mobile sensors in Antwerp, Belgium. The experimental results show that the proposed method outperforms a series of commonly used methods including Ordinary Kriging (OK), Inverse Distance Weighting (IDW) and Random forest (RF).
Qin, X, Do Huu, T, Hofman, J , Rodrigo Bonet, E , Panzica La Manna, V , Deligiannis, N & Philips, W 2021, Street-level Air Quality Inference Based on Geographically Context-aware Random Forest Using Opportunistic Mobile Sensor Network . in International Conference on Innovation in Artificial Intelligence (ICIAI 2021).. Association for Computing Machinery (ACM), pp. 1-10, 2021 International Conference on Innovation in Artificial Intelligence, China, 5/03/21 .
Qin, X., Do Huu, T., Hofman, J. , Rodrigo Bonet, E. , Panzica La Manna, V. , Deligiannis, N. , & Philips, W. (Accepted/In press). Street-level Air Quality Inference Based on Geographically Context-aware Random Forest Using Opportunistic Mobile Sensor Network . In International Conference on Innovation in Artificial Intelligence (ICIAI 2021). (pp. 1-10). Association for Computing Machinery (ACM).
@inproceedings{135b66ae4248482a9c69a49d34d4c4af,
title = " Street-level Air Quality Inference Based on Geographically Context-aware Random Forest Using Opportunistic Mobile Sensor Network " ,
abstract = " The spatial heterogeneity and temporal variability of air pollution in urban environments make air quality inference for fine-grained air pollution monitoring extremely challenging. Most of the existing work estimates the air quality using sparse measurements collected from a limited number of fixed monitoring stations. In this work, we propose a geographically context-aware random forest model for street-level air quality inference using high spatial resolution data collected by opportunistic mobile sensor network. Compared with traditional random forest model, the proposed method builds a local model for each location by considering the neighbors in both geographical and feature space. The model is evaluated on our real air quality dataset collected from mobile sensors in Antwerp, Belgium. The experimental results show that the proposed method outperforms a series of commonly used methods including Ordinary Kriging (OK), Inverse Distance Weighting (IDW) and Random forest (RF). " ,
keywords = " air quality inference, Internet of Things, smart city, machine learning " ,
author = " Xuening Qin and {Do Huu}, Tien and Jelle Hofman and {Rodrigo Bonet}, Esther and {Panzica La Manna}, Valerio and Nikos Deligiannis and Wilfried Philips " ,
year = " 2021 " ,
month = mar,
day = " 5 " ,
language = " English " ,
pages = " 110 " ,
booktitle = " International Conference on Innovation in Artificial Intelligence (ICIAI 2021). " ,
publisher = " Association for Computing Machinery (ACM) " ,
address = " United States " ,
note = " null Conference date: 05-03-2021 Through 08-03-2021 " ,
url = " http://www.iciai.org/html/2021.html " ,
}