摘要
洪水是全世界范围内的主要灾害之一。在暴雨季节,由于各种径流成分汇入河道,当河床的槽蓄量无法容纳大量的水时,就会产生洪水。水库已在世界范围内作为减轻洪水的工程措施之一。水库的功能是可以容纳多余的水,确保流向下游地区的水量低于河道的安全容量。由于社会的需要,水库也有其他用途,如供水和灌溉。既要保持水库有一定水位的蓄水量以满足用水,同时又要在洪水到来前需要释放水量,为来洪预留空间,这种冲突给水库管理者在作出放水决定时带来困难。为此,提出一种基于上游降雨模式的水库洪水水位预测模型。该模型可用于在水库达到最大蓄水量前的时段,以便水库管理者提早作出决策。研究证明,所采用的人工神经网络模型能够在其精度方面满足实际需求。
Flood is one of the major disasters in the world.In the rainstorm season,due to various runoff components into the river channel,when the channel storage of the river bed can not accommodate a large amount of water,flood will occur.Reservoir has been used as one of the engineering measures to reduce flood in the world.The function of the reservoir is to accommodate excess water and ensure that the water flow to the downstream area is lower than the safe capacity of the river channel.Due to the needs of society,the reservoir also has other uses,such as water supply and irrigation.It is necessary to maintain a certain water level of the reservoir to meet the water demand.At the same time,the storage capacity should be released before the flood to reserve space for the coming flood.This kind of conflict brings difficulties to the reservoir managers when they make the decision to release water.This paper presents a reservoir flood level prediction model based on upstream rainfall model.The model can be used in the period before the reservoir reaches the maximum capacity,so that the reservoir managers can make decisions in advance.In this study,it is proved that the artificial neural network model can meet the actual demand in its accuracy.
作者
夏力哈尔·俄坦
Shalihar·OTAN(Yili River Basin Development and Construction Administration Bureau,Yili 835000,Xinjiang,China)
出处
《水利科技与经济》
2021年第3期53-56,共4页
Water Conservancy Science and Technology and Economy
关键词
水库
洪水预报
人工神经网络
降雨模式
reservoir
flood forecast
artificial neural network
rainfall model
