# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 2 Related Research  
Various time series prediction models for weather forecasting have been studied. [PERSON] [3] utilized deep learning models using AirNet, which is pollution and weather time series data for future PM\({}_{10}\) predictions. In this study, RNN, LSTM, and GRU were used for time series data learning, and GRU performed best because only part of AirNet was used due to resource problems.  
[PERSON] [4] suggested deep learning-based Weather Normalized Models (WNM) to quantify air quality changes during the partial shutdown period due to the COVID-19 pandemic in Quito, Ecuador. Deep learning algorithms used CNN, LSTM, RNN, Bi-RNN, and GRU, among which WNM using LSTM and Bi-RNN performed the best.  
Moreover, [PERSON] [5] presented an LSTM model that adds intermediate variable signals to LSTM memory blocks to predict the weather in the Indonesian airport region and explores various architectures such as single- and multilayer LSTM. The proposed model showed that the intermediate variable could enhance the model's predictive ability. The best LSTM model and intermediate data were the multilayer LSTM model and pressure variable, respectively.  
[PERSON] [6] proposed a hybrid model based on CNN and LSTM to predict PM\({}_{2.5}\) per hour in Beijing, China. Based on the hybrid model, we use CNN to extract spatial and internal properties for input values and apply time series data for extracted values using LSTM. The suggested model performed better than the existing deep learning models (i.e., LSTM, Bi-LSTM, GRU, and Bi-GRU).  
The studies mentioned earlier use time series prediction models, such as RNN, LSTM, and GRU, similar to our research but do not use multimodal data in contrast to our approach.  
Few works adopt multimodal data for weather forecasting prediction performance. [PERSON] [7] presented a multimodal deep learning model that combines CNN and GRU to perform PM\({}_{2.5}\) predictions in the Wuxi region over 6 h based on data provided by the Wuxi environmental protection agency. The CNN utilizes a one-dimensional convolution layer, which extracts and integrates local variation trends and spatial correlation characteristics of multimodal air quality data. A GRU learns long-term dependencies based on the CNN results. The proposed model performed better than the existing single deep learning model (i.e., Shallow Neural Network, LSTM, and GRU). [PERSON] [8] recommended a custom pretrained inception model to estimate (i.e., classify whether images collected with observations are contaminated) by air pollution in the Skopje region in northern Macedonia using multimodal data composed of weather data retrieved via sensors and image data from Skopje using cameras. The proposed model is a structure that adds a new sub-modelpath to the pretrained inception model that processes image data, receives weather data as input, and connects the result of three fully connected layers and the output processed by the pretrained inception model to the fully connected layer again. It performed better than existing models (e.g., CNN, ResNet, and pretrained inception).  
Our research differs from [PERSON]'s [7] and [PERSON]'s [8] work. While the multimodal data in Xie [7] are both numerical, we use image and numerical data, such as SO\({}_{2}\), PM\({}_{10}\), and wind speed, from various regions. [PERSON]'s work aims to classify images into polluted or unpolluted, utilizing sensory weather data (e.g., temperature). Unlike [PERSON]'s study, our goal is to predict future PM\({}_{10}\) based on the satellite image and numerical weather data.  
This paper configured multimodal data by adding image data because the image data has information on aerosol particle size. Aerosol can determine the possibility of fine dust movement and accumulation due to atmospheric diffusion, which helps predict fine dust. Thus, we tried to increase performance by constructing multimodal data with numerical and image data that affect fine dust.