A Novel Interpretable Deep Learning Model for Ozone Prediction

Author:

Chen Xingguo1ORCID,Li Yang1,Xu Xiaoyan2,Shao Min3ORCID

Affiliation:

1. Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

2. School of the Environment, Nanjing University, Nanjing 210046, China

3. School of Environment, Nanjing Normal University, Nanjing 210023, China

Abstract

Due to the limited understanding of the physical and chemical processes involved in ozone formation, as well as the large uncertainties surrounding its precursors, commonly used methods often result in biased predictions. Deep learning, as a powerful tool for fitting data, offers an alternative approach. However, most deep learning-based ozone-prediction models only take into account temporality and have limited capacity. Existing spatiotemporal deep learning models generally suffer from model complexity and inadequate spatiality learning. Thus, we propose a novel spatiotemporal model, namely the Spatiotemporal Attentive Gated Recurrent Unit (STAGRU). STAGRU uses a double attention mechanism, which includes temporal and spatial attention layers. It takes historical sequences from a target monitoring station and its neighboring stations as input to capture temporal and spatial information, respectively. This approach enables the achievement of more accurate results. The novel model was evaluated by comparing it to ozone observations in five major cities, Nanjing, Chengdu, Beijing, Guangzhou and Wuhan. All of these cities experience severe ozone pollution. The comparison involved Seq2Seq models, Seq2Seq+Attention models and our models. The experimental results show that our algorithm performs 14% better than Seq2Seq models and 4% better than Seq2Seq+Attention models. We also discuss the interpretability of our method, which reveals that temporality involves short-term dependency and long-term periodicity, while spatiality is mainly reflected in the transportation of ozone with the wind. This study emphasizes the significant impact of transportation on the implementation of ozone-pollution-control measures by the Chinese government.

Funder

National Natural Science Foundation of China

Special Science and Technology Innovation Program for Carbon Peak and Carbon Neutralization of Jiangsu Province

Natural Science Foundation of Jiangsu Province

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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