From data to action in flood forecasting leveraging graph neural networks and digital twin visualization.

ABSTRACT: Forecasting floods encompasses significant complexity due to the nonlinear nature of hydrological systems, which involve intricate interactions among precipitation, landscapes, river systems, and hydrological networks. Recent efforts in hydrology have aimed at predicting water flow, floods, and quality, yet most methodologies overlook the influence of adjacent areas and lack advanced visualization for water level assessment. Our contribution is two-fold: firstly, we introduce a graph neural network model (LocalFLoodNet) equipped with a graph learning module to capture the interconnections of water systems and the connectivity between stations to predict future water levels. Secondly, we develop a simulation prototype offering visual insights for decision-making in disaster prevention and p

SUBMITTER: Roudbari NS 

PROVIDER: S-EPMC11316733 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature