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A hybrid data-driven framework for early leak detection that integrates physics-informed simulation with machine learning and explainable analytics is proposed, revealing that pressure-based temporal statistics and spatial gradients are key indicators of leakage.
This review explores the key techniques for explainability in deep visual recognition, including model-agnostic methods such as LIME and SHAP, model-specific approaches like saliency maps and feature visualization, and intrinsically interpretable models like decision trees and rule-based systems.
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