AI & ML
A Hetero-functional Graph State Estimator for Watershed Systems: Application to the Chesapeake Bay
arXiv eess.SY0 views
arXiv:2603.01931v2 Announce Type: replace Abstract: Regional watersheds are systems of systems in which hydrology, land use, and governance interact. Nutrient models such as the Chesapeake Assessment Scenario Tool (CAST) route nutrients from land to the estuary through lumped delivery factors, so the river network that connects sources to the estuary is not represented explicitly. This paper develops a region-independent watershed reference architecture in Hetero-functional Graph Theory (HFGT) and adapts the Weighted Least Squares Error Hetero-functional Graph State Estimator to estimate nutrient flows in that network from uncertain data. The framework is applied to the Chesapeake Bay watershed. A mass-conserving network of land segments, outlet points, and river reaches is built from public geospatial data. CAST applied loads, land-segment edge-of-river loads, and river-to-bay delivery factors enter the estimator as measurements, each with its own error term. CAST-reported loads delivered to the Bay are withheld from estimation and used only to test the estimated delivery. The estimator reproduces the applied loads (R^2=1.00) and the land-segment edge-of-river loads (R^2=0.997) and under-predicts delivery to the Bay by 8.0% for nitrogen and 5.7% for phosphorus. The gap is traced to CAST delivery factors that imply in-river gains, which a mass-conserving network cannot represent, and to land segments whose edge-of-river loads exceed their applied loads. The framework provides a common systems representation for coupling watershed models with infrastructure, economic, and governance models.
Source:https://arxiv.org/abs/2603.01931PreviousGame-theoretic Regulated Decentralized Coordination for Airspace Sector Overload MitigationNextA System-of-Systems Convergence Paradigm for Societal Challenges of the Anthropocene
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