基于BP神经网络的张掖国家湿地公园水域结冰厚度预报模型

Forecasting Model for Ice Thickness in Zhangye National Wetland Park Watershed Based on BP Neural Network

利用2011年12月2012年3月张掖国家湿地公园水域结冰厚度观测资料和张掖观象台的气温、地温等资料,借助BP神经网络可以逼近任意非线性函数的能力和特点,构建了用于短期预报水域结冰厚度的模型,并验证该模型的预报效果。结果表明,BP神经网络预报模型能够对水域结冰厚度进行有效的短期预报,该结冰厚度的预报模型对结冰厚度的预报效果较理想。流动水域结冰厚度预报历史拟合率高达96.8%,模型试报准确率为85.7%;静止水域结冰厚度预测历史拟合率达87.8%,模型试报准确率为80.0%,其性能指标符合实际要求,具有实际应用价值。

A forecast model for the ice thickness is developed using winter ice thickness data observed in Zhangye National Wetland Park watershed and surface air temperature and ground temperature data observed in Zhangye meteorological station from December 2011 to March 2012. This model is based on BP neural network which can approximate any nonlinear function. The forecasting skill of this model is validated by comparing the forecasted ice thicknessresults with the observed ones. The results show that: The frozen thickness of forecasting model is able to have comparatively ideal forecasting effect to frozen thickness,mobile frozen water area thickness forecasting history intends to jointly lead height to amount to 96. 8%,the model tries reporting accurate rate for85. 7%,motionless frozen water area thickness forecast history intends to jointly lead amounting to 87. 8%,the model tries reporting accurate rate for 80. 0%. They have very good actual application value. It is function index accords with actual request. This forecasting model based on BP neural network is of good performance in forecasting the ice thickness.