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 for 85.7%, 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.
LIU Honglan
,
ZHANG Junguo
,
QUE Longkai
,
ZHENG Xuejin
,
BAO Jiazhi
. Forecasting Model for Ice Thickness in Zhangye National Wetland Park Watershed Based on BP Neural Network[J]. Plateau Meteorology, 2014
, 33(3)
: 832
-837
.
DOI: 10.7522/j.issn.1000-0534.2013.00051
[1]Rober J K, Ans P B. Localized precipitation forecasts from a numercal weather prediction model using artificial neural networks[J]. Wea Forecasting, 1998, 13: 1194-1204.
[2]Marzban C, Wit T A. A Bayesian neural network for severehail size prediction[J]. Wea Forecasting, 2001, 16: 600-610.
[3]Hall T, Brooks H E, Doswell C A. Precipitation forecasting using a neural network[J]. Wea Forecasting, 1999, 14: 338-345.
[4]邵月红, 张万昌, 刘永和, 等. BP 神经网络在多普勒雷达降水量的估测中的应用[J]. 高原气象, 2009, 28(4): 846-853.
[5]何峰, 王瑞荣, 王建中, 等. 一种基于BP神经网络的江河潮位短期预测[J]. 长江科学院院报, 2011, 28(1): 21-24.
[6]胡春梅, 陈道劲, 于润玲. BP 神经网络和支持向量机在紫外线预报中的应用[J]. 高原气象, 2010, 29(2): 539-544.
[7]张乐坚, 程明虎, 田付友. 人工神经网络及支持向量机在降雨量预报中的应用[J]. 高原气象, 2010, 29(4): 982-991.
[8]张韧, 蒋国荣, 余志豪, 等. 利用神经网络计算方法建立太平洋副高活动的预报模型[J]. 应用气象学报, 2004, 11(4): 474-483.
[9]金龙, 吴建生, 林开平, 等. 基于遗传算法的神经网络短期气候预测模型[J]. 高原气象, 2005, 24(6): 981-987.
[10]马学款, 普布次仁, 唐叔乙, 等. 人工神经网络在西藏中短期温度预报中的应用[J]. 高原气象, 2007, 26(3): 491-495.
[11]金龙, 罗莹, 王业宏, 等. 月降水量的神经网络混合预报模型研究[J]. 高原气象, 2003, 22(6): 618-623.
[12]赵声蓉. 多模式温度集成预报[J]. 应用气象学报, 2006, 17(1): 52-58.
[13]胡文东, 沈桐立, 丁建军, 等. 卫星资料的非线性反演同化与一次强降水预报试验[J]. 应用气象学报, 2006, 25(2): 249-258.
[14]赵声蓉, 裴海瑛. 客观定量预报中降水的预处理[J]. 应用气象学报, 2007, 18(1): 21-28.
[15]Jin L, Ju W M, Miao Q L. Study on ANN-based multistep prediction model of short-term climatic variation[J]. Adv Atmos Sci, 2000, 17: 157-164.
[16]李国勇. 智能预测控制及其MATLAB实现[M]. 北京: 电子工业出版社, 2010: 16-24.
[17]李学桥, 马莉. 神经网络工程应用[M]. 重庆: 重庆大学出版社, 1996: 37-44.
[18]陈刚, 于丹, 吴迪. MATLAB基础与实例进阶[M]. 北京: 清华大学出版社, 2012: 68-89.
[19]黄嘉佑. 气象统计分析与预报方法[M]. 北京: 气象出版社, 2004: 252-253.