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Hybrid Supervised-unsupervised Modeling For Post-hurricane Private Well Contamination Risk Score Using Empirical Validation And Community-informed Assessment.

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Hybrid Supervised-unsupervised Modeling For Post-hurricane Private Well Contamination Risk Score Using Empirical Validation And Community-informed Assessment.

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Published on: October 11, 2016

Hybrid Supervised-Unsupervised Modeling for Post-Hurricane Private Well Contamination Risk Score Using Empirical

Jilei Lin¹, Jennifer Zhang^2,3, Ellen Wei²

¹Department of Statistics The George Washington University Washington DC USA.

|June 19, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Keywords:

geospatial modeling ground truthing hurricane helene microbial contamination well water

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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

A new data-driven framework quantifies post-hurricane private well contamination risk. Higher risk scores accurately identified areas with elevated total coliform contamination, aiding disaster preparedness.

Area of Science:

Environmental Science
Public Health
Data Science

Background:

Hurricane-related flooding mobilizes contaminants, posing risks to private wells.
Limited well testing and disaster response capacity exacerbate risks for private well users.

Purpose of the Study:

To develop and validate a data-driven framework for quantifying post-hurricane private well contamination risk.
To integrate geospatial variables and machine learning for risk assessment.

Main Methods:

A hybrid machine learning approach combining supervised and unsupervised learning was used.
Seventy-eight geospatial variables were integrated into hazard, vulnerability, and capacity modules.
The framework was applied to western North Carolina post-Hurricane Helene and validated with well testing data.

Main Results:

Higher risk scores were significantly associated with increased total coliform contamination (p=0.006).
The framework demonstrated value in identifying areas with elevated contamination risk.
Predictive performance was moderate, indicating a need for further evaluation.

Conclusions:

The data-driven framework provides a practical tool for identifying high-risk private well areas after extreme weather events.
The framework supports disaster preparedness, prioritizes well testing, and protects public health.
The transferable framework can be adapted to other regions and storm events.