کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4576576 1629971 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
پیش نمایش صفحه اول مقاله
Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms
چکیده انگلیسی

SummaryStreamflow 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.


► We use classification and regression trees for streamflow forecasting, first.
► We employ the support vector regression model as the benchmark model.
► We incorporate the bagging and boosting to advance the prediction accuracy, secondly.
► The results of ensembles are nearly same and superior to the single models.
► Bagged model performs slightly better than the boosted model in the testing phase.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 477, 16 January 2013, Pages 119–128
نویسندگان
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