Adversarial susceptibility analysis for water quality prediction models

Water quality is a critical factor for human health and environmental sustainability. Rapid urbanization and industrialization have led to significant water contamination, increasing the prevalence of waterborne diseases. This study investigates the presence of pathogens in water sources across the Gujarat region, utilizing machine learning models to analyze contamination patterns. Various classifiers, including HistGradientBoosting, Random Forest, AdaBoost, Bagging, Decision Tree, and LSTM, were employed to predict water quality and identify pathogens. Among these, the Random Forest and Bagging classifiers exhibited the highest accuracy at 98.53%. Furthermore, Explainable AI techniques, specifically SHapley Additive exPlanations (SHAP), were used to interpret the significant features influencing contamination levels. The study highlights the need for proactive water quality monitoring and pathogen detection to prevent disease outbreaks. We also evaluate the robustness of our models under adversarial perturbations to simulate real-world sensor noise and data corruption. Results show a performance drop of up to approx. 56% under FGSM and PGD attacks and 10% after adversarial training withstanding the attacks, highlighting the need for resilient AI systems in public health. The models Random Forest and Simple neural network compare the scores with clean accuracy and after adversarial training. The scores are generated for various epsilon values, showing that the machine learning model suffers drastically, whereas the neural network model can withstand attacks with consistent performance.