Forecasting Water Contamination in Rural Zones Using Machine Learning
A machine learning model predicting microbial water contamination in low-resource regions based on rainfall and terrain data.
Background
Waterborne disease remains a major public health risk in rural areas where access to treated water is limited and testing is infrequent.
What We Built
We built a predictive model using historical rainfall, elevation, and well-testing data to forecast when and where microbial contamination is most likely to spike in groundwater supplies.
Modeling & Research
The model was trained using SVM and random forest classifiers, labeling high-risk days based on past contamination outbreaks. Input features included cumulative rainfall, proximity to livestock, and surface runoff potential.
Impact
With minimal inputs, the model identifies high-risk time windows and enables preventative interventions such as chlorination alerts or mobile testing dispatch.
Outcome
Accepted for internal publication under NSRI’s Environmental Research Program (2025).
Background
Waterborne disease remains a major public health risk in rural areas where access to treated water is limited and testing is infrequent.
What We Built
We built a predictive model using historical rainfall, elevation, and well-testing data to forecast when and where microbial contamination is most likely to spike in groundwater supplies.
Modeling & Research
The model was trained using SVM and random forest classifiers, labeling high-risk days based on past contamination outbreaks. Input features included cumulative rainfall, proximity to livestock, and surface runoff potential.
Impact
With minimal inputs, the model identifies high-risk time windows and enables preventative interventions such as chlorination alerts or mobile testing dispatch.
Outcome
Accepted for internal publication under NSRI’s Environmental Research Program (2025).
Background
Waterborne disease remains a major public health risk in rural areas where access to treated water is limited and testing is infrequent.
What We Built
We built a predictive model using historical rainfall, elevation, and well-testing data to forecast when and where microbial contamination is most likely to spike in groundwater supplies.
Modeling & Research
The model was trained using SVM and random forest classifiers, labeling high-risk days based on past contamination outbreaks. Input features included cumulative rainfall, proximity to livestock, and surface runoff potential.
Impact
With minimal inputs, the model identifies high-risk time windows and enables preventative interventions such as chlorination alerts or mobile testing dispatch.
Outcome
Accepted for internal publication under NSRI’s Environmental Research Program (2025).
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