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

Area of Science:

• Hydrology
• Environmental Science
• Data Science

Background:

• Strategic sensor placement is critical for effective flood monitoring, yet optimal site selection in complex river networks like Missouri's is challenging.
• Existing sensor placement research often focuses on water distribution or contamination, with limited approaches for stream sensor networks.

Purpose of the Study:

• To develop a data-driven methodology for identifying 250 optimal sensor locations across Missouri for enhanced flood monitoring.
• To determine the most influential hydrological variables impacting water levels and flooding to guide sensor placement.

Main Methods:

• Utilized hierarchical clustering with graph-encoded hydrological data from 244 USGS gages.
• Analyzed stage time-series data to identify locations with similar fluctuation magnitudes in response to precipitation.
• Employed decision trees to map response similarities to hydrological drivers and cluster potential sites.

Main Results:

• Identified 250 optimal sensor locations based on data-driven analysis of hydrological features.
• The methodology effectively clusters locations with high monitoring value, considering state-wide implementation.
• Graph-based encoding reduced computational complexity, enabling scalability for large geographic areas.

Conclusions:

• The developed data-driven approach offers a flexible and scalable solution for strategic stream sensor placement.
• This methodology enhances flood prediction capabilities and optimizes the efficiency of sensor networks.
• The study addresses a gap in state-wide sensor placement strategies by leveraging hydrological data and advanced analytics.