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This study presents an ensemble learning framework utilizing physics-informed neural networks (PINN) to estimate soil-water characteristic curves (SWCC) for unsaturated seepage modeling, addressing the challenges of conventional methods that rely on predetermined SWCC models. The proposed method adapts to site-specific measurements, allowing for accurate predictions of hydraulic behavior in soil slopes, as demonstrated through both hypothetical and real-world applications. Results indicate the framework

Author

Sahlan Ruhyana

Language

EN