Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction
Forecasting air pollution is considered as an essential key for early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable damages. Predicting pollution in such situations is particu...
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doaj-44910e6c7ffb44c2bdd18d79f08a919a2021-03-30T15:14:51ZengIEEEIEEE Access2169-35362021-01-019147651477810.1109/ACCESS.2021.30524299328097Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality PredictionIchrak Mokhtari0https://orcid.org/0000-0001-6993-9612Walid Bechkit1https://orcid.org/0000-0002-5438-4033Herve Rivano2https://orcid.org/0000-0001-6112-7468Mouloud Riadh Yaici3CITI Laboratory, INSA Lyon, Villeurbanne, FranceCITI Laboratory, INSA Lyon, Villeurbanne, FranceCITI Laboratory, INSA Lyon, Villeurbanne, FranceCITI Laboratory, INSA Lyon, Villeurbanne, FranceForecasting air pollution is considered as an essential key for early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable damages. Predicting pollution in such situations is particularly challenging due to the strong dynamic of the phenomenon and the various spatio-temporal factors affecting air pollution dispersion. In addition, providing uncertainty estimates of prediction makes the forecasting model more trustworthy, which helps decision-makers to take appropriate actions with more confidence regarding the pollution crisis. In this study, we propose a multi-point deep learning model based on convolutional long short term memory (ConvLSTM) for highly dynamic air quality forecasting. ConvLSTM architectures combines long short term memory (LSTM) and convolutional neural network (CNN), which allows to mine both temporal and spatial data features. In addition, uncertainty quantification methods were implemented on top of our model's architecture and their performances were further excavated. We conduct extensive experimental evaluations using a real and highly dynamic air pollution data set called Fusion Field Trial 2007 (FFT07). The results demonstrate the superiority of our proposed deep learning model in comparison to state-of-the-art methods including machine and deep learning techniques. Finally, we discuss the results of the uncertainty techniques and we derive insights.https://ieeexplore.ieee.org/document/9328097/Conv-LSTMspatio-temporel predictionhighly dynamic air qualityaccidental pollutant releaseuncertaintyFFT-07 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ichrak Mokhtari Walid Bechkit Herve Rivano Mouloud Riadh Yaici |
spellingShingle |
Ichrak Mokhtari Walid Bechkit Herve Rivano Mouloud Riadh Yaici Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction IEEE Access Conv-LSTM spatio-temporel prediction highly dynamic air quality accidental pollutant release uncertainty FFT-07 |
author_facet |
Ichrak Mokhtari Walid Bechkit Herve Rivano Mouloud Riadh Yaici |
author_sort |
Ichrak Mokhtari |
title |
Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction |
title_short |
Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction |
title_full |
Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction |
title_fullStr |
Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction |
title_full_unstemmed |
Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction |
title_sort |
uncertainty-aware deep learning architectures for highly dynamic air quality prediction |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Forecasting air pollution is considered as an essential key for early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable damages. Predicting pollution in such situations is particularly challenging due to the strong dynamic of the phenomenon and the various spatio-temporal factors affecting air pollution dispersion. In addition, providing uncertainty estimates of prediction makes the forecasting model more trustworthy, which helps decision-makers to take appropriate actions with more confidence regarding the pollution crisis. In this study, we propose a multi-point deep learning model based on convolutional long short term memory (ConvLSTM) for highly dynamic air quality forecasting. ConvLSTM architectures combines long short term memory (LSTM) and convolutional neural network (CNN), which allows to mine both temporal and spatial data features. In addition, uncertainty quantification methods were implemented on top of our model's architecture and their performances were further excavated. We conduct extensive experimental evaluations using a real and highly dynamic air pollution data set called Fusion Field Trial 2007 (FFT07). The results demonstrate the superiority of our proposed deep learning model in comparison to state-of-the-art methods including machine and deep learning techniques. Finally, we discuss the results of the uncertainty techniques and we derive insights. |
topic |
Conv-LSTM spatio-temporel prediction highly dynamic air quality accidental pollutant release uncertainty FFT-07 |
url |
https://ieeexplore.ieee.org/document/9328097/ |
work_keys_str_mv |
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1724179737415254016 |