Improving Streamflow Prediction Using Multiple Hydrological Models and Machine Learning Methods

Abstract Streamflow prediction is crucial for flood monitoring and early warning, which often hampered by bias and uncertainties arising from nonlinear processes, model parameterization, and errors in meteorological forecast. We examined the utility of multiple hydrological models (VIC, H08, CWatM, Noah‐MP, and CLM) and machine learning (ML) methods to improve streamflow simulations and prediction. The hydrological models (HMs) were forced with observed meteorological data from the India Meteorological Department (IMD) and meteorological forecast from the Global Ensemble Forecast System (GEFS) to simulate flood peaks and flood inundation areas. We used Multiple Linear Regression, Random Forest (RF), Extreme…

• DO
  Department of Science and Technology, Ministry of Science and Technology, India
• MO
  Ministry of Earth Sciences
• II
  Indian Institute of Technology Madras