A smart dashboard for forecasting disaster casualties: An investigation from sustainable development dimensions

Natural disasters are known to cause widespread and severe damages all over the world annually. Flood events are responsible for economic and human life losses[1]. One of the most important indicators of the damage level in a flood crisis is the number of casualties. This index is evaluated annually in all countries based on natural disasters. Studies indicate that the death rate caused by floods correlates with countries' development over time [2]. In the present study, quantitative values of three sustainability indicators were extracted in the Czech Republic, Iran, and the United States between 1990 and 2020. These indicators are the Human Development Index (DPI), Gross Domestic Product(GDP), and Climate-Change Impacts (CCI), representing the Social, Economic, and Environmental aspects of sustainable development, respectively.  Then, the mathematical relationships between the development indicators and the number of human losses caused by disasters were evaluated using statistical distributions based on time series. In the final step, using Artificial Intelligence (AI) methods, including Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Random Tree (RT), a prediction of the number of potential fatalities per natural disaster was obtained. The outcomes showed that each country's deaths caused by natural disasters could depend on different parameters and impact coefficients. In addition, the ANFIS algorithm, with more than 98% accuracy, has the most efficiency in determining the severity of the event. With the help of this AI system, it is possible to evaluate society's behavior and its resilience against floods from a holistic viewpoint[3].