https://scholar.google.com.ph/citations?user=F4gKTMkAAAAJ&hl=en
Title: Physics-enhanced machine learning for earthquake-induced landslide hazard mapping
Abstract: This project will introduce participants to advanced methods in landslide hazard mapping by coupling classical geotechnical models (Newmark's method for sliding displacements) with physics-informed machine learning (PINN) approaches.
Participants will:
· Explore how seismic shaking, slope stability, and soil parameters interact to trigger landslides,
· Develop machine learning models that incorporate physical constraints (e.g., Newmark's formulas) directly into the learning process,
· Compare traditional data-driven models with PINN-augmented models for landslide susceptibility mapping.
The group will blend theory, numerical simulation, and practical machine learning coding to better predict and understand earthquake-induced landslides.
Prerequisites:
· Basic knowledge of differential equations, soil mechanics, and slope stability,
· Familiarity with machine learning (Linear regression, SVM, RF, XGBoost, etc),
· Experience in Python programming (preferably with PyTorch or scikit-learn),
· Interest in geophysics, hazard modeling, and numerical simulation is a must.
Group members
Riski KURNIAWAN
Zulfaidil ZULFAIDIL
Visarut HUAYSHELAKE
Ruthlyn VILLARANTE
Lolly Jade ROSIL
Gio GONZALES
Mike Andre PUSPUS
Jay Melvin SEGALES