Orienting people-centred disaster shelter planning based on risk assessing with semi-supervised learning

Abstract

Climate-related disasters have been escalating worldwide, incurring major losses. Landslides have become one of the most destructive disasters, especially in China's mountainous areas. To address this, constructing emergency shelters and designing evacuation routes are critical to ensure public safety and minimize impacts on affected residents. This study proposes a novel two-phase, people-centric approach. The first phase applies stakeholder theory and risk economics to develop a landslide hazard assessment model considering vulnerable populations. The model effectively classified 28 landslide points in Bazhou Town, with 35.71 % deemed high-risk and low-risk 64.29 %, reflecting the model's comprehensive risk differentiation capability. The second phase utilizes public choice theory to construct a bi-level multiple objective programming (BLMOP) model addressing conflicting government and resident goals. The algorithm produced 4 Pareto-optimal shelter plans for each village assessed, results demonstrate the proposed approach generates shelter plans meeting government aims while maximizing resident satisfaction, accounting for local conditions. Grounded in field data and a people-focused lens, this two-stage methodology provides a multiple objective optimization framework balancing stakeholder need. A case study of Bazhou Town validates the method's effectiveness.