Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

Erdal H. I., Karakurt O.

JOURNAL OF HYDROLOGY, vol.477, pp.119-128, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 477
  • Publication Date: 2013
  • Doi Number: 10.1016/j.jhydrol.2012.11.015
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.119-128
  • Keywords: Bagging (bootstrap aggregating), Classification and regression trees, Ensemble learning, Stochastic gradient boosting, Streamflow prediction, Support vector regression, NEURAL-NETWORK ENSEMBLES, MACHINE
  • Gazi University Affiliated: Yes


Streamflow forecasting is one of the most important steps in the water resources planning and management. Ensemble techniques such as bagging, boosting and stacking have gained popularity in hydrological forecasting in the recent years. The study investigates the potential usage of two ensemble learning paradigms (i.e., bagging; stochastic gradient boosting) in building classification and regression trees (CARTS) ensembles to advance the streamflow prediction accuracy. The study, initially, investigates the use of classification and regression trees for monthly streamflow forecasting and employs a support vector regression (SVR) model as the benchmark model. The analytic results indicate that CART outperforms SVR in both training and testing phases. Although the obtained results of CART model in training phase are considerable, it is not in testing phase. Thus, to optimize the prediction accuracy of CART for monthly streamflow forecasting, we incorporate bagging and stochastic gradient boosting which are rooted in same philosophy, advancing the prediction accuracy of weak learners. Comparing with the results of bagged regression trees (BRTs) and stochastic gradient boosted regression trees (GBRTs) models possess satisfactory monthly streamflow forecasting performance than CART and SVR models. Overall, it is found that ensemble learning paradigms can remarkably advance the prediction accuracy of CART models in monthly streamflow forecasting. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.