Improvements to GMU iFlood using Machine Learning for Real-time Flood Modeling Corrections

Real-time flood modeling is increasingly important given the increased frequency and intensity of severe storms and flood damage. Machine learning provides unique opportunities for improving modeling outcomes, adjusting model outputs in real-time which can then be used as input data to inform subsequent predictions. In this work, we focus on improving George Mason University’s (GMU) iFlood Integrated Flood Forecast System. iFlood currently provides high-accuracy flood forecasts from twice-daily runs over the tidal region of the Potomac River from Lesieta to Little Falls, covering the Washington Metropolitan region and including coastal areas of the National Capital, Alexandria, and Arlington. iFlood has been operating for multiple years and is currently included in local forecast ensembles used by local weather forecasters to make valuable flood assessments. Our results explore how various machine learning techniques can be used to alter flood predictions, assessing impacts on model outputs as well as changes to computational dependencies.