Future outlooks in applications and prioritisation strategies for the use of Artificial intelligence in natural disaster management

Nowadays, artificial intelligence (AI) enables smarter and faster crisis management decisions. With the help of these models, preventive measures in critical situations (such as early warning systems (EWS), evacuation planning, and resource allocation) are facilitated, and the fundamental challenges associated with natural disasters are easier to address. The study aimed to use various research, case studies, and practical reports to examine the usefulness of AI in different stages of disaster management. The present study offered a comprehensive exploration of AI applications in natural disasters. The search for related articles (during ۲۰۰۰-۲۰۲۴) was performed using relevant academic databases in English, e.g., Scopus, Web of Science, and Science Direct. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, ۱۰۸ studies were included in the quantitative synthesis. Finally, ۷۳ articles related to the topic were selected for more detailed analysis. In addition to exploring the motivations, recommendations, challenges, and limitations of recent advancements, the research provides a specific AI applications taxonomy in natural disasters. Besides, it provided an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution lays the groundwork for future AI-based natural disaster management works.

کلیدواژه ها:

Long short-term memory ، Recurrent neural networks ، Forecasting ، Crisis communication