August 11, 2025

Integral Assessment of the Social Risk of Emergency Situations Caused by Floods

Key Points

Systematic assessment of societal risk in floods reveals persistent threats to life and safety, especially in exposed populations.
The study outlines a new framework with metrics and models for evaluating the societal risk of floods influenced by environmental factors.
Utilizing hydrology methods, machine learning, and mathematical modeling, the approach focuses on community-specific emergency management.
Implications emphasize the necessity for integrated flood responses, highlighting urgency in developing community preparedness initiatives.

Abstract

Every year, the management bodies of the unified state system for emergency prevention and elimination carry out advance preparations for the flood period. However, statistics do not show a decrease in the number of deaths and injuries in floods. This situation is due to the lack of an integrated approach to the systematic assessment and consideration of a set of interrelated factors that determine the social risk of death in floods. A scientific and methodological apparatus for the integral assessment of the societal risk caused by floods was developed. The study is based on the analysis and synthesis of the results of theoretical research, as well as the classification and formalization of the results of empirical research. The scientific and methodological apparatus includes fundamental concepts, metrics of societal risk, models and methods that provide a systematic and multifactorial approach to such an assessment. An algorithm for obtaining an integral indicator of the level of societal risk of such emergencies for a municipality is described, which includes three models: a model of the environment as a digital twin of the area prone to floods; a model of probabilistic assessment of flood hazard depending on the natural and climatic conditions of the modeling region, as well as a digital model of the population of a municipality exposed to the damaging factors of flood water. The algorithm takes into account the probability of timely evacuation of the population during a flood. Finally, the parametric law of human injury through the probability of loss of stability in flood water has been determined. The research was based on the use of hydrology methods, geoinformatics, mathematical modeling, descriptive and predictive statistics, and machine learning. The developed scientific and methodological apparatus can be used in municipalities when solving issues of local importance aimed at protecting the population and the territory of the settlement from emergencies caused by floods.

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