Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization

Abstract

• Proposed XGB-based ML model for RFFA and flood estimation. • CAMELs data for 671 catchments from the USA were used to test the efficacy of the approach. • Multiple Linear Regression methods and Artificial Neural Networks were used to compare the output of the eXtreme Gradient Boost-based ML model. • The importance of each catchment feature is characterized using Gini Impurity, Permutation, and Dropout Loss Feature Ranking methods. Regional Flood Frequency Analysis (RFFA) is one of the widely used approaches for estimating design floods in the ungauged basins. We developed an eXtreme Gradient Boost (XGB) machine learning model for RFFA and flood estimation. Our approach relies on developing a regression model between flood quantiles and the commonly available catchment descriptors. We used CAMELs data for 671 catchments from the USA to test the approach's efficacy. The results were compared with the traditional Multiple Linear Regression methods and Artificial Neural Networks. Results revealed that the XGB-based approach estimated design flood with the highest accuracy during training and validation with minor mean absolute error, root mean square error values, and percentage bias ranging from −10 to + 10. The importance of each catchment feature is visualized by three different approaches Gini Impurity, Permutation, and Dropout Loss Feature Ranking. We observed that the most dominating variables are rainfall intensity, slope, snow fraction, soil porosity, and temperature. It is observed that the importance of these variables is a function of the hydroclimatic regions and varies with space. In contrast, mean annual areal potential evapotranspiration, mean annual rainfall, fraction forest area, and soil conductivity have low significance in estimating design flood for an ungauged catchment. Indeed, the proposed XGB-based approach has broader applicability and replicability.