Utilizing a machine learning technique to evaluate and map the susceptibility to multi-hazard

Sujung Heo1,2, Sangjin Park3, Hyewon Kang4, Ji Yeon Kim5, Yong Piao3 and Dong Kun Lee6, (1)Interdisciplinary program in Landscape Architecture, Seoul, South Korea, (2)Seoul National University, Seoul, South Korea, (3)Seoul National Universiy, Interdisciplinary Program in Landscape Architecture & Integrated Major in Smart City Global Convergence, Seoul, South Korea, (4)Seoul National Universiy, Seoul, South Korea, (5)Seoul National University, Interdisciplinary program in Landscape Architecture, Seoul, South Korea, (6)Seoul National University, Department of Landscape Architecture and Rural System Engineering, Seoul, South Korea