Predicting air quality with deep learning LSTM: Towards comprehensive models

Navares, Ricardo; Aznarte, José Luis

doi:10.1016/j.ecoinf.2019.101019

Cited by 109 publications

(34 citation statements)

References 29 publications

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“…The data used comprise hourly observations from January 1st, 2017, to December 31st, 2018, and includes three meteorological variables and the concentration of particulate matter . Where the latter is considered to be an agent that, when released into the environment, causes damage to ecosystems and living beings 29 , 30 . For this study, the hourly data, recorded at five air quality monitoring stations (see Fig.…”

Section: Methodsmentioning

confidence: 99%

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Sci Rep

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The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of $$\hbox {PM}_{10}$$ PM 10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate $$\hbox {PM}_{10}$$ PM 10 concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.

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Section: Methodsmentioning

confidence: 99%

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Sci Rep

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“…Various work been published in the last few years around the use of deep learning for air quality prediction. Navares and Aznarte [ 26 ] implemented Long Short-Term Memory (LSTM) to predict PM 10 and other air pollutants. They demonstrated a Recurrent Neural Network (RNN) that can map input sequences to output sequences by including the past context into its internal state, making it suitable for time-series problems.…”

Section: Related Workmentioning

confidence: 99%

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Wardana

Gardner

Fahmy

2021

Sensors

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Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.

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“…Lima has ten air quality monitoring stations located in the constitutional province of Callao and the north, south, east, and center of Lima. The data used comprise hourly observations from January 1, 2017, to December 31, 2018, and includes three meteorological variables and the concentration of particulate matter PM 10 , considered to be an agent that, when released into the environment, causes damage to ecosystems and living beings 28,29 . For this study, the hourly data, recorded at five air quality monitoring stations (see Figure 2), which are managed by the National Service of Meteorology and Hydrology (SENAMHI), was considered.…”

Section: Data Understandingmentioning

confidence: 99%

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Preprint

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The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.

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Predicting air quality with deep learning LSTM: Towards comprehensive models

Cited by 109 publications

References 29 publications

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

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