Development of a Urban Flood Prediction Model Using SOM-LSTM: Integrating Environmental IoT and Sewer Water Level Rising Rates

Driven by global climate change, extreme weather events leading to short-duration heavy rainfall have emerged as a primary challenge for urban disaster prevention and resilience. Frequent and intense rainfall not only significantly increases the risk of urban pluvial flooding but also disrupts the stable operation of public infrastructure. Traditional drainage system designs often rely on static solutions that are inadequate for coping with the rapid intensity changes and high uncertainty of extreme rainfall, further exacerbating disaster risks in urban areas.

This study integrates advanced data analytics with machine learning to propose a rainfall and flood risk prediction system based on Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM). Leveraging Internet of Things (IoT) technology, the study incorporates high-resolution data (10-minute intervals) from flood-prone communities in Taipei City between 2015 and 2021. The multi-source dataset includes radar reflectivity, meteorological observations, sewer water level monitoring, and historical flood records to build a hydro-meteorological model with strong spatial and temporal representation. Preliminary results indicate that incorporating wind speed and direction data significantly enhances prediction accuracy and reduces uncertainty. Through SOM technology, the system performs refined classification of high-dimensional meteorological data, excelling in identifying extreme rainfall patterns. Combined with LSTM’s capability to capture temporal sequence characteristics, the system predicts rainfall and water level fluctuations. Furthermore, through a monitoring mechanism for sewer water level rise rates, integrating terrain and sewer spatial characteristics to provide localized, dynamic notifications and tailored response recommendations.

By combining AI-driven uncertainty analysis with real-time hydrological monitoring, this research strengthens flood forecasting capabilities under diverse wind field conditions, providing a science-based decision-support framework. The application of this model not only enhances the precision of community-scale flood prevention planning but also offers an adaptive regional warning strategy for urban climate adaptation. Ultimately, this system will effectively bolster urban disaster resilience and provide local governments with robust decision-support tools to achieve long-term sustainable development goals.