In light of urbanization, climate change, and the escalation of extreme weather events, flood management is becoming more and more important. Improving community resilience and reducing flood risks require prompt decision-making and effective communication. This study investigates how flood management systems can incorporate Large Language Models (LLMs), especially those that use Retrieval-Augmented Generation (RAG) architectures. We suggest a multimodal framework that uses a Flood Knowledge Graph to aggregate data from various sources, such as social media, hydrological, and meteorological inputs. Although LLMs have the potential to be transformative, we also address important drawbacks like governance issues, hallucination risks, and a lack of physical modeling capabilities. When compared to text-only LLMs, the RAG system significantly improves the reliability of flood-related decision support by reducing factual inconsistency rates by more than 75%. Our suggested architecture includes expert validation and security layers to guarantee dependable, useful results, like flood-constrained evacuation route planning. In areas that are vulnerable to flooding, this strategy seeks to strengthen warning systems, enhance information sharing, and build resilient communities.

Object tracking is a crucial aspect for maritime operations and navigation for underwater vehicles. Forward-looking sonar images, which provide high-resolution views of the underwater environment, enable the identification and classification of objects. However, the interpretation of these sonar images can be challenging due to feature distortion, reverberation and environmental noise, complicating object detection. To address these issues, in this paper we propose an enhanced method for underwater multi-object tracking that utilizes deep learning techniques and vehicle motion data. Our object detection framework encompasses range-setting, sliding window preprocessing, and deep learning-based multi-object detection that leverages visual features from sonar images. This process is further enhanced by integrating vehicle motion data collected from navigation devices and sensors. We introduce a combinatory approach for multi-object tracking of static objects, which integrates sonar images with vehicle motion information. The algorithm determines object locations by calculating trajectories in the sonar images using navigation and vehicle movement data and is updated with the object detection algorithm based on visual features in the sonar image. The inclusion of vehicle motion data significantly enhances the precision, recall and processing time of object tracking, ensuring continuous tracks even when objects are temporarily not seen in sonar images. Therefore, our proposed combinatory procedure enhances the algorithm’s robustness against common sonar image challenges, enabling reliable multi-object tracking of static objects in sonar images and supporting subsequent navigational tasks.