The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning
Vinh Ngoc Tran·Valeriy Y. Ivanov·Phong Le·Jongho Kim·Tam Nguyen·Tien Giang Nguyen·P. J. Restrepo·Donghui Xu
Abstract Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines. This study provides the first assessment of ML performance against actual operational forecasting systems operated by the California Nevada River Forecast Center (CNRFC), which combines the Community Hydrologic Prediction System (