Modern Management of Natural Disasters with an Artificial Intelligence Approach Using Fuzzy Logic and Data Mining Processes

The present study examines the applications of artificial intelligence in the management of natural disasters and the associated challenges. The aim of this research is to identify and analyze the role of artificial intelligence, particularly deep learning algorithms and convolutional neural networks (CNNs), in improving the prediction and identification of natural disasters. The findings indicate that remote sensing technologies play a key role in detecting wildfires and predicting earthquakes, and they can assist in enhancing firefighting operations and damage assessment. However, challenges such as data quality and accessibility in underserved areas, as well as limitations in the generalizability of deep learning models, require further attention and improvement. Optimizing early warning systems (EWS) using artificial intelligence and explainable artificial intelligence (XAI) can enhance the processes of prediction and response to disasters. This research also suggests that the use of risk assessment systems, such as the Bowtie method, can help visualize causal relationships in high-risk scenarios. Ultimately, this study serves as a reference for future research in the field of natural disaster management and the optimization of early warning systems, and it can contribute to increasing the resilience of vulnerable communities.