@inproceedings{d8ba784c79d8499fa9226e5decbad1ed,
title = "A comparative study of three deep learning models for PM2.5 interpolation",
abstract = "PM2.5 is a pollutant particulate matter with diameter less than 2.5 micrometer. There exist many stations installed in the world to measure its concentration. Some areas without any proper equipment nor any station installation must rely on interpolation techniques to approximate its concentration. So, there is a need of interpolation technique to approximate the concentration of the pollutant in those areas. The faster and more accurate interpolation technique can help identify more polluted areas and thus efficiently take some measures to reduce PM2.5 harmful effects. We explored three different neural networks, i.e., Bidirectional-Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Neural Networks (TCN), to interpolate the PM2.5 concentration over the southeast region of the U.S. We investigate different data preprocessing techniques and the effects of spatiotemporal correlation on the models. We finally compare these models and make a choice on the model that is more appropriate for PM2.5 interpolation.",
keywords = "Bidirectional-Long Short-Term Memory, Fine particle matter PM2.5, Gated Recurrent Unit, Interpolation, Temporal Convolutional Neural Networks",
author = "Lixin Li and Weitian Tong and Adolphe Some",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 26th International Database Engineered Applications Symposium, IDEAS 2022 ; Conference date: 22-08-2022 Through 24-08-2022",
year = "2022",
month = aug,
day = "22",
doi = "10.1145/3548785.3548809",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "16--24",
editor = "Desai, {Bipin C.} and Revesz, {Peter Z.}",
booktitle = "26th International Database Engineered Applications Symposium, IDEAS 2022",
address = "United States",
}