Air Pollution Forecasting Using CNN-LSTM Deep Learning Model
Journal
2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)
Date Issued
2021-09-27
Author(s)
Jovova, Lenche
DOI
10.23919/mipro52101.2021.9596860
Abstract
One of the greatest issues modern urban environments are facing is poor air quality. It directly affects human health having a long-term negative impact on people's lives and is a major cause of deaths in the world. Smart cities combined with advances in deep learning provide a novel platform for dealing with this problem. This paper uses pollution data from smart sensor networks and a CNN-LSTM architecture to forecast the air pollution concentration of the current hour based on the previous 24-hour pollution concentration and several meteorological features from the previous hour. Initially data is preprocessed with special focus and strategy for handling missing values. The performance of the model is fine-tuned by taking into account additional temporal and seasonal dependency of this type of data. Comparison with other models from classical machine learning shows that the proposed deep learning model has better performance according to the provided metrics.