Explainable artificial intelligence–driven SHAP-based feature selection for interpreting black-box fuzzy modeling: an autonomous decision-making framework

Abstract

Integrating explainable artificial intelligence (XAI) for water quality assessment (WQA) is necessary to protect human health, ensure the availability of clean water and sustainable conservation of the environment. Black-box machine learning (ML) models perform well but lack cognitive insight, limiting their use in decision-support systems. This research addresses the need by putting forward a concise, explainable technique for binary classification of water quality. The research presents a comprehensive autonomous decision-making framework for assessing water quality, incorporating XAI, feature selection and fuzzy IF-THEN reasoning. To ensure the integrity of the statistical analysis, missing data has been addressed through the implementation of multiple imputation by chained equations (MICE). Effective use of highly performing classifier facilitated the predictions. Shapley additive explanations (SHAP) were adopted to identify and normalize significant characteristics. To boost interpretation, fuzzy linguistic terms are generated using arcsinh-based quartile partitioning, facilitating the formation of SHAP-driven fuzzy IF–THEN rules. The validity of the rules has been monitored by activation strength analysis to confirm the consistency between fuzzy inference and predictions of the models. Outperforming all other models, the Random Forest algorithm scored highest accuracy of almost 78%. Through the integration of SHAP computation, the most influential criteria of water quality were determined. The fuzzy modeling process was made simpler by rule creation based on the most essential variables with time complexity reduction of 46.9%. The established rules offered clear conclusions about the evaluation of water potability and successfully translate complicated ML results into indicators that humans can grasp. Selection of model with high accuracy, activation strength analysis of rules, inter-fold consistency in SHAP ranks demonstrates that the suggested framework attains both predictive and interpretative stability. The proposed XAI–SHAP black-box fuzzy model facilitates the development of autonomous decision-making framework for public health, water quality and environmental sustainability. Despite the scientific merits of soundness and understanding of the described SHAP-fuzzy architecture, numerous limitations have to be admitted. The RF model had experienced medium predictive accuracy with an accuracy of about 78% throughout cross-validation. The standard deviation is relatively small indicating the model is stable and is always generalizing correctly. On the other hand, it may be possible to enhance the classification effectiveness with better advanced data pre-processing, dealing overlapping of criteria and class imbalance and use of integrated models, which have a higher predictive strength. The framework can support water management authorities in making faster, more transparent decisions about water quality. By combining explainable AI with fuzzy reasoning, it helps non-experts understand why certain assessments are made, improving trust and accountability. It can be applied to real-time monitoring systems to detect contamination risks early and prioritize interventions. The approach also enables efficient resource allocation by focusing on the most influential parameters. The framework can improve public health by enabling earlier detection of water contamination and more reliable quality assessments. Its transparency helps build trust among communities, regulators and stakeholders by clearly explaining decisions. Better water management can support equitable access to safe water, particularly in vulnerable regions. However, disparities in data availability and technical infrastructure may widen gaps between well-resourced and underserved areas. In contrast to traditional methods that utilize SHAP exclusively as an interpretation instrument, the suggested framework developed a trustworthy methodology by integrating a transparent connection between ML explanations and linguistic fuzzy reasoning. The proposed XAI–SHAP fuzzy model facilitates the development of decision-making framework for public health, water quality and environmental sustainability. It uniquely combines SHAP-based feature normalization, arcsinh-based quartile partitioning, understandable IF-THEN fuzzy rules and activation strength analysis. This guarantees both interpretability and stability, offering a clear and elucidative method for binary classification of water quality.