A Framework for Integrated Geohazard Risk Assessment and Adaptation

Thesis Abstract

The study addresses the escalating challenge of geohazards in densely populated regions by proposing an integrated framework that synthesizes hazard identification, risk assessment, and adaptation planning to inform proactive decision-making under uncertainty. The aim is to develop and validate a holistic framework that (i) characterizes spatial-temporal geohazards, (ii) quantifies multi-hazard risk exposure for vulnerable communities, and (iii) embeds adaptive strategies within governance and land-use planning to reduce loss and enhance resilience. Specific objectives include (a) to integrate geotechnical, seismotectonic, hydrological, and climatic data into a unified geohazard catalog; (b) to develop a multi-criteria risk scoring model combining exposure, vulnerability, and recovery capacity across prioritized study sites; (c) to formulate an adaptation toolkit comprising structural and non-structural measures, disruptors of cascading failures, and early-warning protocols; (d) to test the framework via scenario-based simulations and participatory workshops with stakeholders; and (e) to evaluate cost-effectiveness and social acceptability of proposed measures under climate and urban development scenarios. The methodological design is a mixed-methods study conducted in three metropolitan catchments with documented geohazard activity. The population includes municipal planners, geotechnical engineers, emergency managers, and residents within flood, landslide, and seismic-prone zones (n=450 survey respondents; 30 key informants). A stratified random sampling approach yields 200 survey respondents for quantitative analysis and 30 semi-structured interviews for qualitative insights. Data collection instruments comprise a geohazard risk assessment questionnaire, a remote sensing-augmented hazard inventory form, and a stakeholder workshop protocol. Instrument validity and reliability are established through content validity by a panel of five domain experts and a pilot test (n=40). Quantitative data are analyzed using regression-based risk scoring, multi-criteria decision analysis (MCDA) with ELECTRE/AHP integrations, and Bayesian updating to incorporate expert priors. Qualitative data from interviews are analyzed thematically using a codebook derived from the literature, with triangulation against survey results. A coupled GIS platform is used to visualize hazard layers, risk indices, and adaptation options. The model specification includes a structural equation model linking hazard intensity, exposure, and vulnerability to overall risk, plus an agent-based component simulating community decision-making under different adaptation portfolios. Economic analysis employs cost-benefit and cost-effectiveness analyses for adaptation pathways, supplemented by a risk-utility assessment to capture social preferences. Ethical considerations follow international standards for risk research, including informed consent, data anonymization, and stakeholder consultative processes. Expected findings indicate that integrating geohazard data with socio-economic indicators yields a robust risk index that identifies hotspots beyond conventional hazard maps. The MCDA framework is anticipated to produce adaptive portfolios prioritizing nature-based solutions, land-use zoning, and early-warning system enhancements that demonstrate superior resilience gains under multiple climate scenarios. The Bayesian framework is expected to improve risk estimates over time as new data accrue, and the scenario simulations should reveal critical leverage points where modest investments yield disproportionate resilience benefits. The study factors in uncertainty propagation across hazard, exposure, and vulnerability components, clarifying where data quality limitations most influence decision outcomes. The study contributes to knowledge by (i) operationalizing an integrative, transferable geohazard risk framework that explicitly links hazard science with resilience planning, (ii) advancing methodological integration of MCDA, Bayesian updating, and agent-based modeling within geohazard contexts, and (iii) providing an adaptable toolkit for policymakers, practitioners, and communities to co-create risk-informed adaptation strategies. The main conclusion posits that a coupled framework combining quantitative risk metrics with participatory adaptation planning enhances both the efficiency and legitimacy of geohazard management. Recommendations emphasize routine updating of hazard inventories, capacity-building for local authorities in data-driven decision-making, incorporation of equity considerations in adaptation prioritization, and the establishment of a regional knowledge-sharing platform to sustain iterative learning and governance agility.