# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 1 Introduction  
As science and technology advance daily, the quality of life has improved, but air pollution is also increasing in various forms. Fine dust causes or worsens symptoms, such as heart- and lung-related diseases [1]. Among many studies on air pollution, studies on predicting fine dust have been conducted using prediction models of time series data to prevent such health damage. However, most studies include various types of data (e.g., PM\({}_{10}\), temperature, dew point, and wind speed) but do not include aerosol data. Aerosol refers to fine substances floating in the atmosphere, including fine dust, and it is useful for predicting fine dust movement and accumulation because of atmospheric diffusion.  
In this paper, the numerical dataset that demonstrated the best performance among the datasets constructed in the previous study [2] was referred to, and the aerosol image data highly related to fine dust was added to enhance the model's performance. However, the preconfigured numerical data was in units of hour, so the multimodal dataset was constructed by adding a satellite image with information on aerosol particle size, which was organized by the hour to match this. In addition, the satellite image contains aerosol data of the entire Korean Peninsula, so the overall flow of fine dust on the Korean Peninsula can be observed.  
In this paper, we propose a multimodal deep learning model by combining the Long Short Term Memory (LSTM) series model that shows superior performance in time series data prediction and the Convolutional Neural Network (CNN) series models useful for image processing to learn the configured dataset. The multimodal deep learning model suggested in this paper combines the features of the numerical and image datasets processed by the CNN series model, so it is necessary to process many features. Therefore, theLSTM series model used the LSTM AutoEncoder, which had the best performance when there were many features. In the previous study [2], the CNN series model used a basic CNN and VGGNet (VGG16, VGG19) to compare the performance difference according to network depth. The VGGNet model is a standard deep CNN architecture with multiple layers, developed by the Visual Geometry Group, a research team at Oxford University.  
As a result, the multimodal deep learning models using numerical and image data performed better than the LSTM series model using only numerical data. Among them, the performance was the best when using VGG19, which has the deepest network depth among the CNN series models. In addition, we divided the image data into original and cropped images and applied each to a multimodal deep learning model to compare the performance difference. To maximize the model's performance, the hyperparameter set in the model was optimized using katib, a hyperparameter optimization system. The contents mentioned above are described in detail below.  
The paper is composed as follows. Section 2 describes related research, and Section 3 presents the constructed dataset. Next, Section 4 defines the proposed model for the dataset, and Section 5 explains the hyperparameter optimization of the configured model. Section 6 describes the experiment and ends with the conclusion and future work in Section 7.