# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 1 Introduction
The LSTM and GRU are commonly used in ozone prediction in conjunction with the Encoder-Decoder framework [49]. However, the performance of the Encoder-Decoder is limited by the fixed length of the hidden state. The presence of an attention mechanism [50] breaks the bottleneck of the Encoder-Decoder. With the attention mechanism, certain methods [51; 52] can achieve more accurate results. Besides temporal features, spatial factors are also crucial. Ozone pollution is typically a regional air quality concern. Therefore, it is influenced not only by local emissions and meteorological conditions but also by the long-range transportation of ozone and its precursors [53; 54]. Several deep learning-based networks are widely used to capture spatial information. The CNN can learn shift-invariant features of data because the kernel remains constant during convolution operations. Some researchers have utilized CNN models to analyze images and forecast air pollutant concentrations [55; 56]. However, it is the shared parameters that make the CNN take all monitoring stations as identical within a single convolution. The attention mechanism serves the purpose of learning spatial relationships more directly by assigning different weights to each site. Some methods have been investigated, such as [57], that learn spatiotemporal information from a pre-organized feature matrix. It then applies LSTM and Multilayer Perceptron (MLP) to assign weights in the temporal and spatial dimensions to forecast PM\({}_{2.5}\). Spatiality is an inherent characteristic of graph neural networks, which treats each monitoring station as a node in an undirected graph, with edges representing connections between the nodes The value of an edge indicates the strength of the connection. Using GCNs to forecast air pollutants requires performing convolution in the spectral space, which demands substantial computational power. Furthermore,the initial issue when predicting ozone with GCNs is defining the relationship between two randomly selected monitoring stations. In this paper, we propose a novel method called the Spatiotemporal Attentive Gated Recurrent Unit (STAGRU) based on the Seq2 Seq model and the attention mechanism. Our method aims to predict local ozone concentration in five mega cities across China suffering severe ozone pollution. We utilize spatiotemporal information obtained from in situ observations to improve the precision of our predictions. STAGRU applies two types of attention mechanisms. One captures temporal knowledge from historical data using an RNN-based network, while the other selects a significant moment from the historical data in the surrounding sites for the current prediction. Due to its ability to learn directly from historical data, the proposed model has a lower computational burden and higher interpretability. Details regarding model construction and the data used for training and experimental comparisons are presented in Section 2. Subsequently, we conducted experiments to compare STAGRU with Seq2 Seq and Seq2 Seq+Attention-based models. Finally, we will discuss the interpretability of STAGRU, which offers us insights into temporality and spatiality. Furthermore, a derivative model called STAGRU-Decoder is proposed, which predicts ozone concentration for multiple stations simultaneously.