Regional Flood Inundation Nowcast Using Double-Encoder Transformer

In the context of rapid global population growth and extensive economic development, urbanization is expanding rapidly. The expansion of urbanization brings about increasingly complex challenges for cities, and flooding is one of the disasters faced. Climate change has led to a significant increase in extreme hydrological events, particularly a sharp rise in rainfall intensity, further elevating the risk of flooding in low-lying urban areas. The study area is located in Taipei City, characterized by low-lying terrain surrounded by mountains, and is influenced by subtropical climate. The frequent occurrence of heavy rainfall during the monsoon season and typhoons contributes to frequent flooding events, with the additional impact of climate change increasing the risk of intense rainfall. Therefore, the real-time prediction of regional flooding and its application in urban management becomes an imperative task, aiding in early warning, effective flood risk response, and ensuring sustainable urban development.

This study utilizes the Double-Encoder Transformer model for real-time flood forecasting leveraging dual-encoder architecture to process and analyze diverse data types relevant predicting floods. One encoder could be dedicated to interpreting meteorological data, such as rainfall spatial distribution. This encoder focuses on extracting and understanding the complex patterns in weather-related data, which are crucial for predicting the likelihood of flooding. The second encoder, on the other hand, could handle geographical and environmental data, including terrain topology, and land use patterns. This encoder is adept at understanding how environmental factors contribute to flood risk in specific areas. By concurrently processing these two streams of information, the Double-Encoder Transformer can create a more comprehensive prediction model. It can identify correlations between meteorological conditions and environmental responses, leading to more accurate and timely flood forecasts. This approach enhances the model's ability to predict not only when and where floods might occur but also their potential severity, aiding in disaster preparedness and resource allocation.

Overall, the application of the Double-Encoder Transformer in flood forecasting represents a significant advancement in disaster management, leveraging AI's power to integrate and analyze complex, multi-faceted data for better, more informed decision-making in critical situations.