Stormwater BMP Vulnerability Index Prediction System
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Solution Overview
Problem
Current stormwater best management practices (BMPs) have not been adequately investigated for their effectiveness in reducing waterborne disease prevalence, particularly in underserved communities, despite their known benefits in managing stormwater runoff and improving water quality.
Innovation Solution
A system and method that utilizes data on waterborne disease cases and socioeconomic factors to determine a vulnerability index, simulating the impact of various stormwater BMPs using machine learning models to predict their effectiveness in mitigating vulnerabilities to waterborne diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If stormwater BMPs are implemented to control surface runoff and improve water quality, then water quality and runoff control are improved, but their effectiveness on waterborne disease reduction has not been investigated
Solution Approach 1:
The patent replaces traditional physical monitoring methods with a machine learning-based computational system. The system uses trained models to predict waterborne disease risk by processing socioeconomic and environmental data, substituting mechanical field monitoring with automated digital prediction and evaluation capabilities.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between stormwater BMP implementation and waterborne disease outcomes. The model acts as a predictive bridge, translating BMP characteristics and environmental data into quantified disease risk assessments, enabling evaluation of effectiveness without direct measurement of disease reduction.
2Measurement precision
If machine learning models are used to predict BMP impact on vulnerability index, then prediction capability is improved, but data requirements and model complexity increase
Solution Approach 1:
The patent creates a universal machine learning model that serves multiple functions: predicting vulnerability index, evaluating BMP effectiveness, and prioritizing intervention areas. This multi-functional approach consolidates what could be multiple separate analytical tools into a single system, managing complexity while maintaining comprehensive prediction capabilities.
Solution Approach 2:
The system implements self-service through automated data processing and prediction generation. The machine learning model automatically processes socioeconomic and environmental data to produce vulnerability assessments and BMP effectiveness predictions without requiring manual analysis, reducing operational complexity while maintaining high prediction accuracy.
Data Source
AI summary
Disclosed herein are a system and methods for determining an area's vulnerability to waterborne diseases and evaluating stormwater best management practices (BMPs) to mitigate vulnerabilities, according to various implementations. Generally, the disclosed system is configured to obtain data relating to a number of waterborne disease cases in a given area and corresponding socioeconomic data for the area. Based on these datasets, the disclosed system can determine a vulnerability index that is indicative of the area's vulnerability to waterborne diseases. The system can then predict how various stormwater BMPs can impact the vulnerability index (e.g., positively or negatively) to evaluate how implementing stormwater BMPs can improve the subject area's vulnerability to waterborne diseases. In some cases, the system and methods described herein may be implemented via a software tool; however, other implementations are contemplated herein.