Vulnerability Analysis and Assessment Tool (VAA)

Systemic risks management calls for Integrated Catastrophe Analysis and Management Modeling approaches ISCRiMM. Several important aspects and components of the ISCRiMM include considerations of socio-economic and structural vulnerabilities of the involved stakeholders and systems, their safety and security constraints, feasible ex-ante and ex-post (strategic and operational) mitigation and adaptation measures, the need for stochastic catastrophe models (scenario generators), and stochastic optimization solution procedures to enable the decision-support for (systemic) risk management. Systemic risks depend on socio-economic and structural vulnerabilities, reliability and resilience of infrastructures, i.e., the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions. In ISCRiMM, the vulnerability module is used to analyze the degree of probability of elements at risk to suffer damages that may result from catastrophes. These can be direct and indirect damages. The socio-economic and structural vulnerabilities can be reduced through improvements of buildings codes and infrastructure safety regulations, construction of shelters, population relocation, loss compensation and reconstruction programs. The location and the level of the implemented measures depend on the goals and constraints of the risk management incorporated in ISCRiMM, e.g., the safety constraints for systemic risks management.

Statistical and machine learning (ML) approaches to the analysis of vulnerabilities effectively supplement traditional vulnerability analysis and modeling methods. The ML models can be used to analyze future socio-economic and structural vulnerabilities based on projections of vulnerability drivers. For example, socio-economic vulnerability can be classified into four main indicator groups: social, educational, housing, and social dependence. The drivers of these indicators are: social – dwelling population density, widows female population in total population, elderly people, female population in total population, room occupancy per household; educational – minimum level of education, unemployed population (inactive population), women with more than 3 children (in total women who gave birth); housing – housing density, average room area per person on census tract, average household room area on census tract, average no. of private/owned houses, number of rooms, etc. For modeling and predicting structural vulnerability, the number of building stories, building material, distance to epicenter, and other variable characterizing structural and physical properties of buildings and earthquakes can be used in statistical methods to explain the damage volume. In some regions, also foundation type, land type, roof type, ground floor type, and superstructure type based on construction materials are considered as predictor variables. The ML models can be trained based on pre-calculated vulnerability indicators and relevant covariates. Then, the ML vulnerability models can be used to study future vulnerabilities assuming different scenarios of predictors.

Trained statistical models serve as Scenario Analysis Tool of plausible future socio-economic and structural vulnerabilities accounting for changing new conditions and projections of relevant covariates, for testing feasible mitigation precautionary and reconstruction “Building-Back-Better (BBB)” measures. The ML approach can reduce the time and costs for new data collection and revision of vulnerability indices.

On the right-hand side, the human vulnerability indices are visualized with the Risk&Vulnerability Scenario (RVS) analysis tool (see Komendantova, N., Ermolieva, T., Zobeidi, T., Armas, I., Toma-Danila, D., Huerlimann, M. forthcoming 2025), the software being developed by Cooperation and Transformative Governance group of Advancnig Systems Analysis Program (CAT-ASA, IIASA) for PARATUS project (Promoting disaster preparedness and resilience by co-developing stakeholder support tools for managing the systemic risk of compounding disasters, here and here).

Involving stakeholders’ opinions, the RVS within ISCRiMM allows for the interactive update of the covariates (vulnerability drivers) and recalculation of the vulnerability indices according to plausible alternative scenarios/projections of population density, income levels, dependency ratios, buildings density, buildings codes, etc., towards decreasing the vulnerabilities and fulfilling the overall ISCRiMM risk management goals.