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Storms occurring in urban areas lead to runoff that overwhelms storm sewer systems. This paper discusses a system for flood risk prediction system in real-time using environmental sensors and deep learning. Synthetic data of temperature, humidity, soil moisture, rainfall, water level, and flow rate were used to train several different models. The LSTM model achieved the highest accuracy. Once the model was trained, it was deployed on an Arduino and ESP32. The environmental sensors send the data to a Flask server which is hosting the LSTM prediction model. The prediction model classifies the flood risk into 3 categories: LOW–MODERATE–HIGH. Once the model makes a prediction, the system alerts and triggers a response for gate control. The LSTM pre-dictions are displayed locally and logged. The system is a low-cost and scalable solution for smart flood management systems.

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An Integrated Multisensor and LSTM-Based Deep Learning Storm Sewer System for Real-Time Flood Risk Prediction and Mitigation

  • 会议论文

  • 发表日期：

    2026年

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  • Koushik Biswas Arko,
  • Mahin Montasir Afif,
  • Md. Mortuza Ahmmed,
  • Md. Ashraful Babu,
  • Mohamed Lahby

摘要

Storms occurring in urban areas lead to runoff that overwhelms storm sewer systems. This paper discusses a system for flood risk prediction system in real-time using environmental sensors and deep learning. Synthetic data of temperature, humidity, soil moisture, rainfall, water level, and flow rate were used to train several different models. The LSTM model achieved the highest accuracy. Once the model was trained, it was deployed on an Arduino and ESP32. The environmental sensors send the data to a Flask server which is hosting the LSTM prediction model. The prediction model classifies the flood risk into 3 categories: LOW–MODERATE–HIGH. Once the model makes a prediction, the system alerts and triggers a response for gate control. The LSTM pre-dictions are displayed locally and logged. The system is a low-cost and scalable solution for smart flood management systems.