Hydrological probabilistic forecasting based on deep learning and Bayesian optimization algorithm

Obtaining accurate runoff prediction results and quantifying the uncertainty of the forecasting are critical to the planning and management of water resources. However, the strong randomness of runoff makes it difficult to predict. In this study, a hybrid model based on XGBoost (XGB) and Gaussian pr...

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Detalhes bibliográficos

Principais autores:	Haijun Bai, Guanjun Li, Changming Liu, Bin Li, Zhendong Zhang, Hui Qin
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2021-08-01
coleção:	Hydrology Research
Assuntos:	bayesian optimization algorithm gaussian process regression probabilistic forecasting runoff xgboost
Acesso em linha:	http://hr.iwaponline.com/content/52/4/927
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