  This study addresses the vital issue of real-time flood detection and
management. It innovatively combines advanced deep learning models with Large
language models (LLM), enhancing flood monitoring and response capabilities.
This approach addresses the limitations of current methods by offering a more
accurate, versatile, user-friendly and accessible solution. The integration of
UNet, RDN, and ViT models with natural language processing significantly
improves flood area detection in diverse environments, including using aerial
and satellite imagery. The experimental evaluation demonstrates the models'
efficacy in accurately identifying and mapping flood zones, showcasing the
project's potential in transforming environmental monitoring and disaster
management fields.
