A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities

By Chiou-Jye Huang, Ping-Huan Kuo. Italian National Conference on Sensors, 2018

630 citations recorded by the source corpus.

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

Subfields: Building and Construction, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/72e64193afde2bdba3fdd6b06ba955e6d235ec5a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. M. Hua
2. Xu Wu
3. Taoying Li

In Environmental Engineering

1. M. Hua
2. Xu Wu
3. Taoying Li

In Health, Toxicology and Mutagenesis

1. M. Hua
2. Xu Wu
3. Taoying Li

Citations in the corpus

• A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5) (2020, methodology): “[25] verified feasibility and practicability of the CNN-LSTM model to predict the PM2.”
• PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data (2019, background): “The missing values in our study area were very high, and thus CNN and LSTM could not be applied.”
• Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow (2020, methodology): “The use of convolutional neural network (CNN)–LSTM, which combines CNN with LSTM, has also been proven to be excellent in forecasting particulate matter 2.5 (PM 2.5 ) [19].”
• County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model (2019, methodology): “Inspired by the success of CNN and RNN, a CNN-LSTM network was proposed in the study, which mainly consists of 2-Dimensional Convolutional neural networks (Conv2D) and LSTM networks [55].”