Disaster impact forecasting for risk-informed decision making with socioeconomic indices: evidence from earthquake-tsunami in India

The risk-informed decision-making relies on the risk assessment results for reducing and managing the disaster risk. The general structure of decision models for risk-informed decision-making is based on measuring disaster risk across various decision scenarios. The disaster risk measurements are integrating the estimated exposure and disaster impacts with probabilistic assessments of natural hazards’ occurrence and co-occurrence. As such, it is crucial to estimate the impact of disaster under different conditions. The estimation of disaster impact should take into account the dynamic changes of the risk drivers (including the variables that can be affected by disaster risk management, DRM, strategies) as well as the decision criteria that decision makers can use to reduce and manage disaster risk. In other words, the disaster impact needs to be forecasted over time while considering the risk factors under different DRM strategies. In addition to physical characteristics, some of the disaster risk factors are socioeconomic variables (e.g., national and sub-national gross income, population density, etc.), which have their own dynamics over time. Once the causal effects of these variables on the disaster impact are determined, they can be included in the disaster impact forecasting models. This study presents a forecast framework for short-term disaster impact and its connection to different decision models for risk-informed decision-making. The theoretical disaster impact forecasting framework is used to investigate the role of socioeconomic variables in forecasting the impact of earthquakes and tsunamis in India. The results show the statistical significance of the variables in the human development index (HDI) as well as the subnational vulnerability index, SGVI, (published by Global Data Lab, GDL). These results show the predictive causality of socioeconomic factors and provide a platform for tracking the cascading impacts and sequential decision-making.