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 policy-making. This prototype visualizes predicted water levels and facilitates data analysis using decades of historical information. Focusing on the Greater Montreal Area (GMA), particularly Terrebonne, Quebec, Canada, we apply LocalFLoodNet and prototype to demonstrate a comprehensive method for assessing flood impacts. By utilizing a digital twin of Terrebonne, our simulation tool allows users to interactively modify the landscape and simulate various flood scenarios, thereby providing valuable insights into preventive strategies. This research aims to enhance water level prediction and evaluation of preventive measures, setting a benchmark for similar applications across different geographic areas.