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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Predicting stream water quality under different urban development pattern scenarios with an interpretable machine

Runzi Wang¹, Jun-Hyun Kim², Ming-Han Li²

¹School for Environment and Sustainability, University of Michigan, 440 Church Street, Ann Arbor, MI 48109-1041, United States of America.

The Science of the Total Environment

|December 29, 2020

Summary

Urban sprawl degrades stream water quality. Machine learning models predict that high-density development can reduce nitrate and phosphate pollution but may increase E. coli during wet seasons.

Keywords:

Landscape metrics Machine learning Scenario planning Urban form Urban sprawl Water quality

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Area of Science:

Environmental Science
Urban Planning
Water Quality Management

Background:

Urban development patterns influence pollutant dynamics in streams.
Understanding these impacts is crucial for effective urban planning and policy-making.
Previous studies highlight the link between urbanization and water quality degradation.

Purpose of the Study:

To predict stream water quality under different urban development scenarios.
To identify key urban development pattern metrics impacting water quality.
To provide empirical evidence on the consequences of urban sprawl for stream health.

Main Methods:

Collected pollutant data (nitrate, total phosphate, E. coli) from 1047 sites in the Texas Gulf Region.
Employed Random Forest (RF) machine learning for scenario prediction.
Utilized SHapley Additive exPlanations (SHAP) and Geographically Weighted Regression (GWR) for impact analysis.

Main Results:

SHAP identified Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Splitting Index (SPLIT), and Landscape Division Index (DIVISION) as critical metrics.
RF predictions indicated aggregated development reduces total phosphate (TP) and nitrate (NO3-N) but may increase E. coli in wet seasons.
Spatial variations in pattern impacts depend on pollutants, seasonality, climate, and urbanization.

Conclusions:

Stream water quality degradation is linked to urban sprawl.
Machine learning offers a powerful tool for forecasting environmental impacts in land-use planning.
Findings support informed urban growth planning to mitigate water quality issues.