Data-driven decision support tools for assessing the vulnerability of community water systems to groundwater contamination in Los Angeles County

Abstract

Regulatory bodies that monitor community water systems (CWS) can be strained for time and resources, making it difficult to take science-informed preventative action before contamination affects local populations. This research aims to develop an easy to deploy, data-driven method to identify CWS that are vulnerable to drinking water quality degradation from groundwater contamination. We focus on a case study of Los Angeles County (LA County) in California to explore the utility of machine learning methods, specifically random forest models (RF) and artificial neural networks (ANN), as quickly deployable decision support tools based on publicly available data. We also aim to provide insight into which factors contribute to vulnerability to groundwater contamination, which may be useful in understanding how vulnerability to contamination emerges. The results of this analysis, which are based entirely on publicly available data, can help policymakers and planners target specific systems, as well as better tailor the type of support needed. We find that both RF and ANN methods can produce relatively low prediction errors but differ in what they predict and how they weigh the relative importance of input variables. The results also suggest that model results can provide stakeholders with a starting point for prioritizing at-risk service areas, but it is important to remember that the model results are most useful in combination with expert opinion.