Artificial hummingbird algorithm-optimized boosted tree for improved rainfall-runoff modelling

Abstract

Rainfall-runoff modelling is a critical component of hydrological studies, and its accuracy is essential for water resource management. Recent advances in machine learning have led to the development of more sophisticated rainfall-runoff models, but there is still room for improvement. This study proposes a novel approach to streamflow modelling that uses the artificial hummingbird algorithm (AHA) to optimize the boosted tree algorithm. the AHA-boosted tree algorithm model was compared against two established methods, the support vector machine (SVM) and the Gaussian process regression (GPR), using a variety of statistical and graphical performance measures. The results showed that the AHA-boosted tree algorithm model significantly outperformed the SVM and GPR models, with an R2 of 0.932, RMSE of 5.358 m3/s, MAE of 2.365 m3/s, and MSE of 28.705 m3/s. The SVM model followed while the GPR model had the least accurate performance. However, all models underperformed in capturing the peak flow of the hydrograph. Evaluations using both statistical and graphical performance measures, including time series plots, scatter plots, and Taylor diagrams, were critical in this assessment. The results suggest that the AHA-boosted tree algorithm could potentially be a superior alternative for enhancing the precision of rainfall-runoff modelling, despite certain challenges in predicting peak flow events.