EWAIS: An Ensemble Learning and Explainable AI Approach for Water Quality Classification Toward IoT-Enabled Systems DOI Open Access

Nermeen Gamal Rezk,

Samah Alshathri, Amged Sayed Abdelmageed Mahmoud

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(12), P. 2771 - 2771

Published: Dec. 5, 2024

In the context of smart cities with advanced Internet Things (IoT) systems, ensuring sustainability and safety freshwater resources is pivotal for public health urban resilience. This study introduces EWAIS (Ensemble Learning Explainable AI System), a novel framework designed monitoring assessment water quality. Leveraging strengths Ensemble models Artificial Intelligence (XAI), not only enhances prediction accuracy quality but also provides transparent insights into factors influencing these predictions. integrates multiple models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, Voting (VEL)—to classify as drinkable or non-drinkable. The system incorporates techniques handling missing data statistical analysis, robust performance even in complex datasets. To address opacity traditional Machine models, employs XAI methods such SHAP LIME, generating intuitive visual explanations like force plots, summary dependency plots. achieves high predictive performance, VEL model reaching an 0.89 F1-Score 0.85, alongside precision recall scores 0.85 0.86, respectively. These results demonstrate proposed framework’s capability to deliver both accurate predictions actionable decision-makers. By providing interpretable system, supports informed management strategies, contributing well-being populations. has been validated using controlled datasets, IoT implementation suggested enhance city environments.

Language: Английский

Unveiling the Hidden Connections: Using Explainable Artificial Intelligence to Assess Water Quality Criteria in Nine Giant Rivers DOI
Sourav Kundu,

P. K. Datta,

Puja Pal

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144861 - 144861

Published: Jan. 1, 2025

Language: Английский

Citations

1
Explainable AI for permeate flux prediction in forward osmosis: SHAP interpretability and theoretical validation for enhanced predictive reliability DOI

Yinseo Song,

Jeongwoo Moon,

Kiho Park

et al.

Desalination, Journal Year: 2025, Volume and Issue: unknown, P. 118551 - 118551

Published: Jan. 1, 2025

Language: Английский

Citations

0
Interpretability Analysis of Data Augmented Convolutional Neural Network in Mineral Prospectivity Mapping Using Black-Box Visualization Tools DOI
Yue Liu, Tao Sun,

Kaixing Wu

et al.

Natural Resources Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

Citations

0
Towards Safer Water: AI-Driven Predictive Analytics for Disease Detection DOI Creative Commons

Jaya Zalte,

Harshal Shah

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Abstract Water quality is a critical factor for human health and environmental sustainability. Rapid urbanization industrialization have led to significant water contamination, increasing the prevalence of waterborne diseases. This study investigates presence pathogens in sources across Gujarat region, utilizing machine learning models analyze contamination patterns. Various classifiers, including HistGradientBoosting, Random Forest, AdaBoost, Bagging, Decision Tree, LSTM, were employed predict identify pathogens. Among these, Forest Bagging classifiers exhibited highest accuracy at 98.53%. Furthermore, Explainable AI techniques, specifically SHapley Additive exPlanations (SHAP), used interpret features influencing levels. The highlights need proactive monitoring pathogen detection prevent disease outbreaks.

Language: Английский

Citations

0
EWAIS: An Ensemble Learning and Explainable AI Approach for Water Quality Classification Toward IoT-Enabled Systems DOI Open Access

Nermeen Gamal Rezk,

Samah Alshathri, Amged Sayed Abdelmageed Mahmoud

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(12), P. 2771 - 2771

Published: Dec. 5, 2024

In the context of smart cities with advanced Internet Things (IoT) systems, ensuring sustainability and safety freshwater resources is pivotal for public health urban resilience. This study introduces EWAIS (Ensemble Learning Explainable AI System), a novel framework designed monitoring assessment water quality. Leveraging strengths Ensemble models Artificial Intelligence (XAI), not only enhances prediction accuracy quality but also provides transparent insights into factors influencing these predictions. integrates multiple models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, Voting (VEL)—to classify as drinkable or non-drinkable. The system incorporates techniques handling missing data statistical analysis, robust performance even in complex datasets. To address opacity traditional Machine models, employs XAI methods such SHAP LIME, generating intuitive visual explanations like force plots, summary dependency plots. achieves high predictive performance, VEL model reaching an 0.89 F1-Score 0.85, alongside precision recall scores 0.85 0.86, respectively. These results demonstrate proposed framework’s capability to deliver both accurate predictions actionable decision-makers. By providing interpretable system, supports informed management strategies, contributing well-being populations. has been validated using controlled datasets, IoT implementation suggested enhance city environments.

Language: Английский

Citations

2