Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

Several studies in environmental engineering emphasize the importance of air quality forecasting for sustainable development around the world. In this paper, we studied a new approach for air quality forecasting in Busan metropolitan city. We proposed a convolutional Bi-Directional Long-Short Term Memory (Bi-LSTM) autoencoder model trained using a distributed architecture to predict the concentration of the air quality particles (PM2.5 and PM10). The proposed deep learning model can automatically learn the intrinsic correlation among the pollutants in different location. Also, the meteorological and the pollution gas information at each location are fully utilized, which is beneficial for the performance of the model. We used multiple one-dimension convolutional neural network (CNN) layers to extract the local spatial features and a stacked Bi-LSTM layer to learn the spatiotemporal correlation of air quality particles. In addition, we used a stacked deep autoencoder to encode the essential transformation patterns of the pollution gas and the meteorological data, since they are very important for providing useful information that can significantly improve the prediction of the air quality particles. Finally, in order to reduce the training time and the resource consumption, we used a distributed deep leaning approach called data parallelism, which has never been used to tackle the problem of air quality forecasting. We evaluated our approach with extensive experiments based on the data collected in Busan metropolitan city. The results reveal the superiority of our framework over ten baseline models and display how the distributed deep learning model can significantly improve the training time and even the prediction accuracy.

Download Full-text

Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system

10.5194/gmd-2019-346 ◽

2020 ◽

Author(s):

Ebrahim Eslami ◽

Yunsoo Choi ◽

Yannic Lops ◽

Alqamah Sayeed ◽

Ahmed Khan Salman

Keyword(s):

Deep Learning ◽

Wavelet Transform ◽

Air Quality ◽

Dynamic Time Warping ◽

Learning Algorithm ◽

Post Processing ◽

Time Warping ◽

Deep Learning Algorithm ◽

Dynamic Time ◽

Air Quality Forecasting

Abstract. As the deep learning algorithm has become a popular data analytic technique, atmospheric scientists should have a balanced perception of its strengths and limitations so that they can provide a powerful analysis of complex data with well-established procedures. Despite the enormous success of the algorithm in numerous applications, certain issues related to its applications in air quality forecasting (AQF) require further analysis and discussion. This study addresses significant limitations of an advanced deep learning algorithm, the convolutional neural network (CNN), in two common applications: (i) a real-time AQF model, and (ii) a post-processing tool in a dynamical AQF model, the Community Multi-scale Air Quality Model (CMAQ). In both cases, the CNN model shows promising accuracy for ozone prediction 24 hours in advance in both the United States and South Korea (with an overall index of agreement exceeding 0.8). For the first case, we use the wavelet transform to determine the reasons behind the poor performance of CNN during the nighttime, cold months, and high ozone episodes. We find that when fine wavelet modes (hourly and daily) are relatively weak or when coarse wavelet modes (weekly) are strong, the CNN model produces less accurate forecasts. For the second case, we use the dynamic time warping (DTW) distance analysis to compare post-processed results with their CMAQ counterparts (as a base model). For CMAQ results that show a consistent DTW distance from the observation, the post-processing approach properly addresses the modeling bias with predicted IOAs exceeding 0.85. When the DTW distance of CMAQ-vs-observation is irregular, the post-processing approach is unlikely to perform satisfactorily. Awareness of the limitations in CNN models will enable scientists to develop more accurate regional or local air quality forecasting systems by identifying the affecting factors in high concentration episodes.

Download Full-text

Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system

Geoscientific Model Development ◽

10.5194/gmd-13-6237-2020 ◽

2020 ◽

Vol 13 (12) ◽

pp. 6237-6251

Author(s):

Ebrahim Eslami ◽

Yunsoo Choi ◽

Yannic Lops ◽

Alqamah Sayeed ◽

Ahmed Khan Salman

Keyword(s):

Deep Learning ◽

Wavelet Transform ◽

Air Quality ◽

Dynamic Time Warping ◽

Learning Algorithm ◽

Post Processing ◽

Time Warping ◽

Deep Learning Algorithm ◽

Dynamic Time ◽

Air Quality Forecasting

Abstract. As the deep learning algorithm has become a popular data analysis technique, atmospheric scientists should have a balanced perception of its strengths and limitations so that they can provide a powerful analysis of complex data with well-established procedures. Despite the enormous success of the algorithm in numerous applications, certain issues related to its applications in air quality forecasting (AQF) require further analysis and discussion. This study addresses significant limitations of an advanced deep learning algorithm, the convolutional neural network (CNN), in two common applications: (i) a real-time AQF model and (ii) a post-processing tool in a dynamical AQF model, the Community Multi-scale Air Quality Model (CMAQ). In both cases, the CNN model shows promising accuracy for ozone prediction 24 h in advance in both the United States of America and South Korea (with an overall index of agreement exceeding 0.8). For the first case, we use the wavelet transform to determine the reasons behind the poor performance of CNN during the nighttime, cold months, and high-ozone episodes. We find that when fine wavelet modes (hourly and daily) are relatively weak or when coarse wavelet modes (weekly) are strong, the CNN model produces less accurate forecasts. For the second case, we use the dynamic time warping (DTW) distance analysis to compare post-processed results with their CMAQ counterparts (as a base model). For CMAQ results that show a consistent DTW distance from the observation, the post-processing approach properly addresses the modeling bias with predicted indexes of agreement exceeding 0.85. When the DTW distance of CMAQ versus observation is irregular, the post-processing approach is unlikely to perform satisfactorily. Awareness of the limitations in CNN models will enable scientists to develop more accurate regional or local air quality forecasting systems by identifying the affecting factors in high-concentration episodes.

Download Full-text