4.3 Article

Hydrological probabilistic forecasting based on deep learning and Bayesian optimization algorithm

期刊

HYDROLOGY RESEARCH
卷 52, 期 4, 页码 927-943

出版社

IWA PUBLISHING
DOI: 10.2166/nh.2021.161

关键词

Bayesian optimization algorithm; Gaussian process regression; probabilistic forecasting; runoff; XGBoost

资金

  1. National Natural Science Foundation of China [51979113]

向作者/读者索取更多资源

A hybrid model combining XGB and GPR with BOA optimization is proposed for runoff probabilistic forecasting, achieving high accuracy and reliability on runoff prediction problems by integrating these two methods and optimizing hyper-parameters using BOA.
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 process regression (GPR) with Bayesian optimization algorithm (BOA) is proposed for runoff probabilistic forecasting. XGB is first used to obtain point prediction results, which can guarantee the accuracy of forecast. Then, GPR is constructed to obtain runoff probability prediction results. To make the model show better performance, the hyper-parameters of the model are optimized by BOA. Finally, the proposed hybrid model XGB-GPR-BOA is applied to four runoff prediction cases in the Yangtze River Basin, China and compared with eight state-of-the-art runoff prediction methods from three aspects: point prediction accuracy, interval prediction suitability and probability prediction comprehensive performance. The experimental results show that the proposed model can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results on the runoff prediction problems.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据