Hybrid physically based and machine learning model to enhance high streamflow prediction

Abstract

ABSTRACT Despite the significant performance of machine learning models for streamflow prediction, their precision for poorly represented data is reduced. This is a concern for flood mitigation purposes where high streamflow values are the most relevant but scarce. Consequently, this study proposes a methodology to create a hybrid model to mitigate the accuracy reduction of a standalone machine learning model in high streamflow values. The hybrid model combines a surrogate model that reproduces a physically based model with a model to estimate its residuals employing the Random Forest algorithm. The hybrid model reaches a root mean squared error reduction of 23% and 33% in the study catchments for values over a three-year return period compared to a standalone machine learning model. The percentage bias decreases by more than 70% from values over a 1.5-year return period. Moreover, the hybrid model has shown close predictions of values higher than the training set.