Harnessing Heterogeneous Sources of Data and Artificial Intelligence for Hydrologic Monitoring

The persistent global threat of water-related challenges, particularly floods, necessitates a paradigm shift towards harnessing new technologies, heterogeneous sources of data, and novel techniques to enhance data availability and innovative sensing techniques in hydrology. Emerging data sources, including ground-based cameras, smart hydrologic monitoring systems, citizen observatories, and crowdsourcing, along with innovative techniques like Artificial Intelligence (AI), provide diverse yet novel data sources for more effective monitoring, modeling, and management. This research contributes to this transformative journey by exploring the integration of real-time imagery data from different tools and sources into hydrologic monitoring. Highlighting our efforts is the development of ATLANTIS, the first comprehensive image dataset for semantic segmentation of water bodies and associated objects. We introduce AQUANet, a state-of-the-art deep neural network crafted for precise waterbody segmentation, addressing challenges such as flood detection and inundation mapping. The study further demonstrates flood modeling using cutting-edge deep learning networks, including PSPNet, TransUNet, and SegFormer. Rigorous comparisons against reference data collected through field instruments and sensors underscore the superior performance of SegFormer, achieving an impressive 99.55% Intersection over Union (IoU) and 99.81% accuracy in hydrological monitoring, specifically in water level estimation at our testbed rivers and channels. In conclusion, this presentation not only showcases achievements in flood monitoring using innovative AI techniques and diverse data sources but also discusses how future studies can contribute to the ongoing discourse on the application of advanced technology in hydrologic monitoring systems, paving the way for further innovation and improvements in flood management.