Air-pollution prediction in smart city, deep learning approach

Abstract

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than $$2.5 \mu m$$ 2.5 μ m ($$PM_{2.5}$$ P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $$PM_{2.5}$$ P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $$PM_{2.5}$$ P M 2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $$PM_{2.5}$$ P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $$PM_{2.5}$$ P M 2.5 concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method “hybrid CNN-LSTM multivariate” enables more accurate predictions than all the listed traditional models and performs better in predictive performance.

Urban Ecological Monitoring and Prediction Based on Deep Learning

“…), learns the representation of this related information through deep learning and machine learning methods such as graph embedding learning, and improves the generalisation performance of single-task models by combining air quality prediction and estimation tasks through multi-task learning [6]. Physical dispersion simulation-based methods estimate the distribution of pollutants by simulating the pattern of pollutant dispersion [7,8]. Linear statistical model-based methods use linear models such as spatial interpolation or land-use regression combined with land-use related characteristics to estimate air quality.…”

Forecasting the impact of meteorological parameters on air pollutants in Andhra Pradesh using machine learning techniques

“…The training set is used to fine‐tune the network until convergence is reached once the model's structure has been built. This piece looks at the MAE, MSE, RMSE, and R 2 (Bekkar et al., 2021, C R et al., 2018). $$$$\begin{equation}{\rm{i}}{\rm{.$$…”

“…For this, we can employ machine learning (ML) models. A computer may learn how to create models with the use of training data via a process known as ML (Bekkar et al., 2021). It is a branch of artificial intelligence that allows computer systems to forecast events with increasing degrees of accuracy.…”

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

“… Bekkar et al (2021) applied a CNN-LSTM deep learning approach to predict air pollution in Beijing. The authors used a dataset of air pollution and air quality concentrations with 13 variables from 12 sites.…”