Short-term Prediction Method of Reservoir Downstream Water Level Under Complicated Hydraulic Influence.

The downstream water level of a reservoir is influenced by its own discharge, changes in external hydraulic conditions, and the value of the previous period's downstream water level, and is very sensitive to hourly changes. However, the influence mechanisms of this change and an accurate prediction method have yet to be investigated. In this study, the downstream water level of Xiangjiaba reservoir in China's Jinsha river was used as a case study to analyze the impact of backwater effects caused by river rising during the flood season and the effect of sharp fluctuations caused by the peak regulation flow during the non-flood season. Moreover, an accurate prediction method at short-term two hourly scale is proposed. This study quantified the backwater effect caused by the rising tributaries of Hengjiang and Minjiang rivers. The random forest algorithm (RF) was used to downscale and rank multidimensional feature data, build different model factor sets, and build a downstream water level prediction model using five different methods. The results showed that the data mining model had the best fit and good prediction ability for the downstream water level of the Xiangjiaba reservoir under the influence of complicated hydraulic factors during the flood season, and can effectively control the fluctuation error during the peak regulation period. The research findings can be applied to other similar basins to improve the reservoir's short-term refined operational levels. [ABSTRACT FROM AUTHOR]