A Study on Abnormal Detection in Urban Inundation Sensor Using LSTM-Autoencoder Techniques

This study aims to improve anomaly detection in urban flooding sensor data by utilizing the Long Short-Term Memory (LSTM)-Autoencoder technique. Flooding problems caused by climate change and rapid urbanization are becoming increasingly frequent in densely populated urban areas, making it crucial to build robust real-time monitoring and rapid response systems. Traditional flood monitoring systems often rely on static thresholds or short-term pattern analysis, which limits their ability to detect complex and subtle anomalies that can develop in sensor data over extended periods of time. To address these limitations, we developed an LSTM-Autoencoder-based anomaly detection model capable of identifying abnormal signals from inundation sensors in real time. The LSTM component excels at processing time-series data with temporal dependencies, while the Autoencoder is characterized by its ability to extract and reconstruct meaningful features of the input data. In this study, we used measurement data collected under both ideal experimental conditions and artificially generated abnormal conditions as training inputs. Data with reconstruction errors exceeding a predefined threshold were classified as anomalies or outliers. The experimental results demonstrated that the proposed model significantly improved inundation prediction accuracy compared to conventional methods, and it consistently maintained high sensitivity under a wide range of environmental changes and unpredictable anomaly scenarios. The LSTM-Autoencoder model developed in this research effectively captures temporal dynamics and variations in sensor data, thereby enhancing the reliability of urban flood monitoring systems. Ultimately, this approach is expected to contribute to more accurate urban flood forecasting and play a key role in advancing smart urban flood management systems.