Prediction of non-carcinogenic health risk using Hybrid Monte Carlo-machine learning approach

Abstract

Groundwater contamination caused by elevated nitrate levels and its associated health effects is a serious global concern. The U.S. Environmental Protection Agency has developed a method for assessing potential human health risks from groundwater contamination that involves extensive groundwater sampling and analysis. However, this approach can be labor intensive and stand as a constraint to the robustness of the traditional approach. Here in machine learning (ML) could be alternative approaches to bridging the contemporary challenges. Machine learning models (ML) such as deep neural networks (DNN), gradient boosting machines (GBM), random forests (RF) and generalized linear models (GLM) can provide alternative solutions to overcome these limitations. In this study, the effectiveness of Hybrid Monte Carlo Machine Learning (MC-ML) models was evaluated by predicting health risks using hazard quotients. A total of 32 groundwater samples were collected and analyzed for nitrate and physical properties during the pre- and post-monsoon seasons. The results showed that the groundwater was severely contaminated by elevated nitrate concentrations, leading to high hazard quotient values. The prediction model results and validation using error and performance metrics showed that the Hybrid MC-DNN model outperformed the other models in both the training and testing phases. These results suggest that this surrogate approach could be a promising alternative to traditional health risk assessment methods.