Graph-based preprocessing and hierarchical clustering for optimal state-wide stream sensor placement in Missouri

Strategic sensor placement is essential for effective flood monitoring and data collection. With numerous candidate sites, Missouri's extensive river network presents a challenge in determining optimal locations. Additionally, various factors influence water levels and flooding, making it crucial to identify which variables have the greatest impact to guide sensor placement. Despite significant research on sensor placement in water distribution systems and contamination monitoring, approaches for stream sensor placement remain notably scarce. This study develops a data-driven approach to pinpoint 250 optimal sensor locations across Missouri. Using hierarchical clustering with graph-encoded hydrological data, we analyze stage time-series data from 244 active USGS gages to identify locations with similar magnitudes of stage fluctuations in response to external influences such as precipitation. The similarity in responses is further mapped to underlying hydrological drivers using a decision tree. The most influential features are subsequently used to cluster potential locations, ensuring placement in areas with the highest monitoring value. Despite the advancement in sensor placement methodologies, there is a lack of studies that consider a state-wide implementation. This study leverages graph-based encoding to reduce computational complexity, making it scalable for large geographic areas. While conventional sensor placement methods in water networks and contamination monitoring rely on predefined variables and optimization techniques, this study offers a flexible, data-driven methodology that captures the most significant hydrological features. This approach aims to enhance flood prediction and improves the efficiency of sensor networks.