Sensor Fusion Enhances Anomaly Detection in a Flood Forecasting System

Abstract

To build an Internet of Things (IoT) infrastructure that provides flood susceptibility forecasts for granular geographic levels, an extensive network of IoT weather sensors in local regions is crucial. However, these IoT devices may exhibit anomalistic behavior due to factors such as diminished signal strength, physical disturbance, low battery life, and more. To ensure that incorrect readings are identified and addressed appropriately, we devise a novel method for multi-stream sensor data verification and anomaly detection. Our method uses time-series anomaly detection to identify incorrect readings. We expand on the state-of-the-art by incorporating sensor fusion mechanisms between nearby devices to improve anomaly detection ability. Our system pairs nearby devices and fuses them by creating a new time series with the difference between the corresponding readings. This new time series is then input into a time-series anomaly detection model which identifies if any readings are anomalistic. By testing our system with nine different machine learning anomaly detection methods on synthetic data based on one year of real weather data, we find that our system outperforms the previous anomaly detection methods by improving F1-Score by 10.8%.