[S-CG64] Integrative seismic and secondary hazard/risk assessment

[SCG64-P06] Physics-Informed and Data-Driven Machine Learning for Earthquake-Induced Landslide Susceptibility Mapping in North Cotabato, Philippines

*JAYROLD PROVENDIDO ARCEDE¹, Gio Gonzales¹, James Earl Cubillas¹, Jay Melvin Segales¹, Vince Nazario², Mike Andre Puspus¹, Hadikka Etabag², Lolly Jade Rosil¹, Mhea Jasmin Recilla¹, Dinah Dumaguing¹, Arnaldo Gagula ¹, Nichole Anthony Pacle¹, Clark Justine Gonzales¹, Genevieve Quisto¹, Marc Lin Abonales¹, Arturo Daag², Margarita Dizon², Rhommel Grutas², Mark Asch³ (1. Caraga State University , 2. Department of Science and Technology-Philippine Institute of Volcanology and Seismology, 3. University of Picardie Jules Verne)

Keywords:Earthquake-induced landslides, Landslide susceptibility mapping, Physics-informed neural networks, Machine learning, Newmark’s method, Seismic hazard