# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 2 Related Work  
[PERSON] et al. [9] built a web application that displays climate changes, natural disaster risks, and socioeconomic conditions. The aim of this web application is to reduce the risk of natural disasters and improve economic efficiency while adapting to weather changes. The application collects data from natural disasters and economic aspects. These data include flood, landslide, and drought risks, and crop production value. In this web application, 2D and 3D views are included. The 2D view displays disaster risk map information and socioeconomic statistics that include the gross regional product and the output of various industries. People can understand the relationship between natural disaster risk and economic output through their changes and develop relevant policies. In addition, the 3D view of the site enhances people's understanding of natural disasters. However, this web application was only on the statistical level and did not use artificial-intelligence techniques to process the data, but just provided a visual interface. In our system, we visualize the changes in weather data and use graph embedding to detect weather anomalies so that people can better understand weather conditions. [PERSON] et al. [10] proposed and built a data visualization tool for treatment-process monitoring to solve the problem of traditional water quality assessment being retrospective and intermediate. The functions of data visualization tools include water quality data aggregation, prediction, and anomaly detection. In data aggregation, the authors first corrected problematic data in the original dataset, including negative, null, and extreme values. They changed extreme values to triple the hourly data's variance, and negative and null values to the mean of the hourly data. The purpose of data aggregation is to ensure visit efficiency and filter peaks to show the average trends of the data. In their tool, two main working modes were included: query mode and analysis mode. The query mode mainly visualizes data. Data visualization includes data series, anomalies, and predictions. The prediction function of this data visualization tool builds a prediction model through the display of time series and model learning. Predicted values can also be provided for the anomaly detection function. If a predicted value exceeds the normal range, it is shown as abnormal. In addition, the density-based anomaly detection method is applied to the anomaly detection function of this tool, which can allow for staff to identify problems and resolve them immediately. However, in the anomaly detection function, this visualization tool just uses the Pearson product moment correlation method to detect anomalies. This anomaly detection method did not extract feature from data and map the original data into a low-dimensional space. In our system, we build graphs from the relationships between multiple time series and embed the graphs into a low-dimensional space to extract features from weather data. [PERSON] et al. [11] compared and analyzed 41 weather data visualization tools from the five aspects of purpose, data content, visual presentation, interactivity, and web technology. Regarding purpose, just over 66% of weather visualization sites supported exploration, and only 17% of sites supported user-simulated weather models. In the data content aspect, environmental change was the most common data type in most systems, with 78% of weather visualization tools showing data on air temperature, precipitation, and extreme weather events. In the data visualization aspect, 88% of the tools visualized weather data on the map. Therefore, the map was the core technology of weather visualization, and it was the most different between weather visualization tools and other visualization tools. For each tool interaction, the exploratory weather visualization tool statistically had 1.6 more operators than those of the interpretive weather visualization tool.