Streamflow Regime Classification and Annual Discharge Prediction for Ungauged Catchments in British Columbia, Canada Using the Extreme Gradient Boosting Algorithm
The prediction of the streamflow regime at ungauged locations within a stream network is a fundamental problem in hydrology. The objective of this project is to assess the ability of the Extreme Gradient Boosting (XGB) algorithm, a newly developed machine learning algorithm, to classify streamflow regimes in BC, Canada. The created is statistically robust and requires only readily available climate and topographic data. However, the ability of the XGB algorithm to predict streamflow quantities is modest. Its performance in numeric prediction is similar to the performance of regression models, and it tends to over-predict for locations with low flow condition.
