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

Fahimeh Sharafkhani1, Steven Corns1, Bong-Chul Seo2

  • 1Engineering Management and Systems Engineering Department, Missouri University of Science & Technology, Rolla, MO, 65409, USA.

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Summary

This study identifies 250 optimal sensor locations for flood monitoring in Missouri using a data-driven approach. It analyzes hydrological data to find sites with similar responses to factors like precipitation, enhancing flood prediction and sensor network efficiency.

Keywords:
Dynamic time-warpingHierarchical graph-based clusteringSensor placementStream sensorsTime-series clustering

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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.