Application of time series analysis techniques in proactive disaster management in the state of Rio de Janeiro - Fernandes, Samir Batista (Seven Editora, 2023)
In this study, an in-depth analysis of time series was conducted to enhance disaster management and contingency planning in the state of Rio de Janeiro. Employing time series analysis techniques, the study aimed not only to validate pre-existing hypotheses but also to discover new avenues for evidence-based decision-making. The analysis revealed distinct seasonal patterns, particularly between November and April, an identified critical period for effective response strategy implementation. The research underscored the significant contribution of specific occurrence categories to the observed trend and seasonality in the time series, providing a solid foundation for reducing uncertainties in contingency planning. Furthermore, the potential for result extrapolation paves the way for more accurate forecasts of future events, facilitating a swifter and more effective response to adverse events. In conclusion, the study serves as a robust model for data-driven disaster management, indicating a path towards a more scientific and effective approach in the development and implementation of contingency plans. It represents a valuable contribution to existing literature, displaying the potential of machine learning and time series analysis in promoting proactive, data-driven disaster management. DOI:https://doi.org/10.56238/interdiinovationscrese-006