Data transformation models utilized in Bayesian probabilistic forecast considering inflow forecasts

Author:

Xu Wei12,Fu Xiaoying2,Li Xia1,Wang Ming1

Affiliation:

1. College of River and Ocean Engineering, National Engineering Research Center for Inland Waterway Regulation, Chongqing Jiaotong University, Chongqing, China

2. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Sichuan, China

Abstract

Abstract This paper presents a new Bayesian probabilistic forecast (BPF) model to improve the efficiency and reliability of normal distribution transformation and to describe the uncertainties of medium-range forecasting inflows with 10 days forecast horizons. In this model, the inflow data will be transformed twice to a standard normal distribution. The Box–Cox (BC) model is first used to quickly transform the inflow data with a normal distribution, and then, the transformed data are converted to a standard normal distribution by the meta-Gaussian (MG) model. Based on the transformed inflows in the standard normal distribution, the prior and likelihood density functions of the BPF are established, respectively. In this study, the newly developed model is tested on China's Huanren hydropower reservoir and is compared with BPFs using MG and BC, separately. Comparative results show that the new BPF model exhibits significantly improved data transformation efficiency and forecast accuracy.

Funder

National Natural Science Foundation of China

Open Fund Approval

Chongqing Science and Technology Commission

Publisher

IWA Publishing

Subject

Water Science and Technology

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