Explainable AI for Safe Water Evaluation for Public Health in Urban Settings

With the rapid improvement of human standards, the development of urban centers has significantly contributed to water contamination and environmental pollution hence compromising the safety of drinking water for public health. The ecological safety and human health have continuously lowered due to hazardous pollution factors like chemicals and pathogens. Different algorithms and blackbox Machine Learning models have been used to develop water quality/safety criteria. This work presents an Explainable Artificial Intelligence method, SHAP (SHapley Additive exPlanations) to transparently and explainably assess the most important metrics that these models use in determining water quality. The simulated results will provide theoretical support to policy makers on how to maintain water quality or safety within urban areas and improve pollution control, water and ecological management.