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Smart city article details
| Title | Ewais: An Ensemble Learning And Explainable Ai Approach For Water Quality Classification Toward Iot-Enabled Systems |
|---|---|
| ID_Doc | 25072 |
| Authors | Rezk N.G.; Alshathri S.; Sayed A.; El-Din Hemdan E. |
| Year | 2024 |
| Published | Processes, 12, 12 |
| DOI | http://dx.doi.org/10.3390/pr12122771 |
| Abstract | In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed for the smart monitoring and assessment of water quality. Leveraging the strengths of Ensemble Learning models and Explainable Artificial Intelligence (XAI), EWAIS not only enhances the prediction accuracy of water quality but also provides transparent insights into the factors influencing these predictions. EWAIS integrates multiple Ensemble Learning models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, and Voting Ensemble Learning (VEL)—to classify water as drinkable or non-drinkable. The system incorporates advanced techniques for handling missing data and statistical analysis, ensuring robust performance even in complex urban datasets. To address the opacity of traditional Machine Learning models, EWAIS employs XAI methods such as SHAP and LIME, generating intuitive visual explanations like force plots, summary plots, dependency plots, and decision plots. The system achieves high predictive performance, with the VEL model reaching an accuracy of 0.89 and an F1-Score of 0.85, alongside precision and recall scores of 0.85 and 0.86, respectively. These results demonstrate the proposed framework’s capability to deliver both accurate water quality predictions and actionable insights for decision-makers. By providing a transparent and interpretable monitoring system, EWAIS supports informed water management strategies, contributing to the sustainability and well-being of urban populations. This framework has been validated using controlled datasets, with IoT implementation suggested to enhance water quality monitoring in smart city environments. © 2024 by the authors. |
| Author Keywords | Explainable Artificial Intelligence; Internet of Things (IoT); Shapley additive explanations; smart city; synthetic minority over-sampling technique; Voting Ensemble Learning; water quality assessment |
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