水库水位预报模型研究
Study on Reservoir Level Forecasting Model
DOI: 10.12677/JWRR.2014.31010, PDF, HTML,  被引量 下载: 2,806  浏览: 7,797  国家科技经费支持
作者: 邓 超, 刘 攀:武汉大学水资源与水电工程科学国家重点实验室,武汉;伍朝晖, 陈 旺:湖北省水文水资源局,武汉
关键词: 水位预报入库流量新安江模型水布垭水库Water Level Prediction; Reservoir Inflow; Xin’anjiang Model; Shuibuya Reservoir
摘要: 水库调度通过水位调控,达到兴利防灾的目的,因此开展水库水位预报具有重要意义。以三水源新安江模型模拟降雨径流关系,利用水库调洪演算原理,构建水库水位预报模型。以水布垭水库为研究对象开展实例研究,结果表明利用建立的水位预报模型无需反推入库流量,水位预报误差满足水文预报精度要求,可有效指导生产实践。
Abstract: The reservoir level prediction is of important significance to the reservoir operation. In this paper, the forecasting model is built to predict the water level in short-term through combining the three-water sources Xin’anjiang model with water balance equation. The proposed forecasting model is applied to analyze the data of Shuibuya reservoir located in Qingjiang River. The result shows that the proposed model can simulate the reservoir inflow well and achieve a satisfactory forecast precision.
文章引用:邓超, 刘攀, 伍朝晖, 陈旺. 水库水位预报模型研究[J]. 水资源研究, 2014, 3(1): 62-65. http://dx.doi.org/10.12677/JWRR.2014.31010

参考文献

[1] 罗时朋, 徐学军, 等. 清江隔河岩库区水位预报[J]. 人民长江, 2003, 34(2): 8-9.
LUO Shipeng, XU Xuejun, et al. Water level forecasting in Geheyan reservoir on Qingjiang River. Yangtze River, 2003, 34(2): 8-9. (in Chinese)
[2] BAZARTSEREN, B., HILDEBRANDT, G. and HOLZ, K.-P. Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing, 2003, 55(3-4): 439-450.
[3] 赵人俊. 流域水文模型——新安江模型和陕北模型[M]. 北京: 水利电力出版社, 1984.
ZHAO Renjun. Hydrological model—Xin’anjiang model and Shanbei model. China Water & Power Press, Beijing, 1984.
[4] 包为民. 水文预报[M]. 北京: 中国水利水电出版社, 2009.
BAO Weimin. Hydrological forecasting. Beijing: China Water & Power Press, 2009.