岩溶地区径流预报中BP网络的改进

Modified BP Neural Network for Runoff Forecasting in the Karst Area

岩溶地区下垫面复杂,各种岩溶管道、裂隙、溶洞发育使得流域不闭合,地下暗河存在水量交换,而地下水库的调蓄作用,使得流域出口断面总流量与降雨量不成绝对的线性关系。为了克服上述问题带来的岩溶地区降雨径流预报精度低问题,提出了改进的BP网络方法,并通过实例验证了此方法的可行性。以六冲河七星关站断面以上流域的平均日降水量、平均日蒸发量、前期流量作为影响因子,建立了2种预报模型:①传统BP网络模型;②运用SPASS软件筛选BP的影响因子数和调整输入层初始权值,并对逐日径流量资料进行对数处理建立改进的BP网络模型。通过实例分析发现改进的BP网络模型预报效果更好,可以有效地提高大洪峰和小洪峰的预报精度。

Complex landforms in Karst area such as Karst pipes,fissures and Karst caves leads to unclosed valley and ground water exchange.The total flow at the outlet section is not in absolute linear relation with precipitation owing to the storage adjustment of groundwater reservoir.To overcome the low precision of rainfall runoff forecasting,we established a conventional BP network model and a modified BP network model for runoff forecasting.The average daily precipitation,average daily evaporation,and runoff in the earlier stage in the basin upstream of Qixingguan Station at Liuchonghe basin were taken as influencing factors.In the modified model,SPASS software was employed to select the influencing factor numbers and adjust the initial weights in the input layer.Logarithm processing is also performed to deal with daily runoff data.It＇s found that the modified BP model can increase the precision of large flood and small flood forecasting.