An Improved BP Algorithm and Its Application to Precipitation Forecast

Min Jingjing; Sun Jingrong; Liu Huanzhu; Wang Shigong; Cao Xiaozhong

Vol.21, NO.1, 2010

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2010 > 21(1): 55-62

Min Jingjing, Sun Jingrong, Liu Huanzhu, et al. An improved bp algorithm and its application to precipitation forecast. J Appl Meteor Sci, 2010, 21(1): 55-62.

Citation:

Min Jingjing, Sun Jingrong, Liu Huanzhu, et al. An improved bp algorithm and its application to precipitation forecast. J Appl Meteor Sci, 2010, 21(1): 55-62.

Min Jingjing, Sun Jingrong, Liu Huanzhu, et al. An improved bp algorithm and its application to precipitation forecast. J Appl Meteor Sci, 2010, 21(1): 55-62.

Citation:

Min Jingjing, Sun Jingrong, Liu Huanzhu, et al. An improved bp algorithm and its application to precipitation forecast. J Appl Meteor Sci, 2010, 21(1): 55-62.

PDF( 1209 KB)

An Improved BP Algorithm and Its Application to Precipitation Forecast

1.
Key Laboratory for Semi-arid Climate Charge of Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000
2.
China Meteorological Administration Training Center, Beijing 100081
3.
National Meteorological Center, Beijing 100081

Received Date: 2008-11-28
Rev Recd Date: 2009-08-11
Publish Date: 2010-02-28

Abstract

Abstract

Objective forecast of precipitation is difficult because of its complex nonlinear characteristics. In order to enhance the ability of forecasting precipitation, artificial neural network (ANN) method is applied in numerical weather products interpretation. Among different types of ANN, the back propagation (BP) neural network is the most popular and influential one. However, traditional BP algorithm has some limitations such as the difficulties in determining network structure and the learning parameters, poor generalization ability and possibility of misleading to local minimum in learning process, etc. To resolve these problems, an improved algorithm is proposed.Based on T213 numerical forecast products of National Meteorological Center from May to September during 2003—2005, 25 factors are selected in terms of dynamic diagnostic analysis and statistical methods. The precipitation forecasts of operational global models from China National Meteorological Center, Japan Meteorological Agency and German Meteorological Administration are studied. Using the reformative BP algorithm, three grades forecast (≥0.1 mm, ≥10.0 mm, ≥25.0 mm) models are built to forecast 24 hour precipitation of 68 stations over Jiang Huai Basin. During the training process, precipitation samples are randomly divided into two kinds according to a certain proportion, training samples and testing samples. They are used to train the network and to check the error of output respectively so that all parameters are confirmed. By repeating training and learning of network, an optimal network model is obtained. The optimized forecast model is used to forecast precipitation of different grades, times and stations, from May to September during 2006—2007. The forecasting results of improved BP algorithm are compared with those of tradition BP algorithm and numerical models outputs. The average threat score (TS) of improved BP algorithm is the highest; the average false alarm rate (FAR) and missing alarm rate (MAR) of improved BP algorithm are much lower than the others. So the improved BP algorithm is superior and it indicates a potential for more accurate precipitation forecasting.
- artificial neural network (ANN);
- back propagation (BP) algorithm;
- improved algorithm;
- modeling;
- precipitation forecast

FullText(HTML)

References(30)

Figures and Tables

图 1 BP训练过程中陷入局部极小点示意图

Fig. 1 A schematic figure for the immer sion of the web to the local least solution in the BP training process

DownLoad: Full-Size Img PowerPoint

图 2 网络模型的泛化能力示意图

Fig. 2 A schematic figure of gene ralization ability for a network model

DownLoad: Full-Size Img PowerPoint

图 3 2006-2007年5-9月各方法的降水量预报检验评分对比

(a) 平均TS评分，(b) 平均空报率，(c) 平均漏报率

Fig. 3 Comparison of forecast verification for 24-hour precipitation among severalf orecasting methods from May to September during 2006-2007

(a) the average threat score (TS), (b) the ave rage false alarm rate (FAR), (c) the average missing alarm rate (MAR)

DownLoad: Full-Size Img PowerPoint

图 4 2006-2007年5-9月各方法对不低于0.1 mm降水预报TS评分对比

Fig. 4 Comparison of threat score (TS) for 24-hour precipitation (≥0.1 mm) among severalforecasting methods from May to Septembe rduring 2006-2007

DownLoad: Full-Size Img PowerPoint

图 5 2006-2007年5-9月各方法对不低于10.0 mm降水预报TS评分对比

Fig. 5 Comparison of threat score (TS) for 24-hour precipitation (≥10.0 mm) among several for ecasting methods from May to September during 2006-2007

DownLoad: Full-Size Img PowerPoint

图 6 2006-2007年5-9月各方法对不低于25.0 mm降水预报TS评分对比

Fig. 6 Comparison of threat score (TS) for 24-hour precipitation (≥25.0 mm) among severalfor ecasting methods from May to September during 2006-2007

DownLoad: Full-Size Img PowerPoint

References

[1]	Hall T, Brooks H E, Doswell C A.Precipitation forecasting using a neural network.Weather and Forecasting, 1999, 14 :338-345. doi: 10.1175/1520-0434(1999)014<0338:PFUANN>2.0.CO;2
[2]	Jin L, Ju W M, Miao Q L.Study on ANN-based multi-step prediction model of short-term climatic variation.Advances in Atmospheric Sciences, 2000, 17 :157-164. doi: 10.1007/s00376-000-0051-4
[3]	Marzban C, Witt A.A bayesian neural network for severe-hail size prediction.Weather and Forecasting, 2001, 16 :600-610. doi: 10.1175/1520-0434(2001)016<0600:ABNNFS>2.0.CO;2
[4]	施丹平.人工神经网络方法在降水量级中期预报中的应用.气象, 2001, 27(6):40-42. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200106007.htm
[5]	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.应用气象学报, 2004, 11(4):474-483. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20000469&flag=1
[6]	李法然, 周之栩, 陈卫锋, 等.湖州市大雾天气的成因分析及预报研究.应用气象学报, 2005, 16(6):794-803. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=200506103&flag=1
[7]	赵声蓉.多模式温度集成预报.应用气象学报, 2006, 17(1):52-58. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20060109&flag=1
[8]	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.应用气象学报, 2006, 25(2):249-258. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200602010.htm
[9]	刘德, 李晶, 刘永华, 等.BP神经网络在长期天气过程预报中的应用实验.气象科技, 2006, 34(3):250-253. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200603002.htm
[10]	赵声蓉, 裴海瑛.客观定量预报中降水的预处理.应用气象学报, 2007, 18(1):21-28. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070104&flag=1
[11]	叶笃正, 曾庆存, 郭裕福.当代气候研究.北京:气候出版社, 1991 :164-177.
[12]	刘还珠, 赵声蓉, 陆志善, 等.国家气象中心气象要素的客观预报———MOS系统.应用气象学报, 2004, 15(2):181-191. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040223&flag=1
[13]	陆如华, 何于班.卡尔曼滤波方法在天气预报中的应用.气象, 1994, 20(9):41-46. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX409.008.htm
[14]	寗春蓉, 冯汉中.利用卡尔曼滤波方法释用数值预报产品.四川气象, 2004, 24(2):16-19. http://www.cnki.com.cn/Article/CJFDTOTAL-SCCX200402004.htm
[15]	陈豫英, 刘还珠, 陈楠, 等.基于聚类天气分型的KNN方法在风预报中的应用.应用气象学报, 2008, 19(5):564-572. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20080507&flag=1
[16]	曾晓青, 邵明轩, 王式功, 等.基于交叉验证技术的KNN方法在降水预报中的试验.应用气象学报, 2008, 19(4):471-477. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20080411&flag=1
[17]	陈永义, 俞小鼎, 高学浩, 等.处理非线性分类和回归问题的一种新方法 (Ⅰ)———支持向量机方法简介.应用气象学报, 2004, 15(3):345-354. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040344&flag=1
[18]	冯汉中, 陈永义.处理非线性分类和回归问题的一种新方法 (Ⅱ)———支持向量机方法在天气预报中的应用.应用气象学报, 2004, 15(3):355-365. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040345&flag=1
[19]	赵国令, 肖科丽.支持向量机在天气预报中的应用.陕西气象, 2004(6):1-4. http://www.cnki.com.cn/Article/CJFDTOTAL-SLXB200606006.htm
[20]	燕东渭, 孙田文, 杨艳, 等.支持向量机数据描述在西北暴雨预报中的应用试验.应用气象学报, 2007, 18(5):676-681. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX200705013.htm
[21]	罗坚.基于SVM的分类方法及其应用.浙江工贸职业技术学院学报, 2008, 8(3):29-33. http://www.cnki.com.cn/Article/CJFDTOTAL-ZJGM200803009.htm
[22]	曹均阔, 舒远仲, 叶水生.一种结构自组织的改进BP网络.计算机工程, 2005, 31(17):165-167. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJC200517061.htm
[23]	王正武, 张瑞平, 刘松.BP网络的一种改进学习方法.数学理论与应用, 2005, 25(1):31-34. http://www.cnki.com.cn/Article/CJFDTOTAL-LLYY200501007.htm
[24]	李宏刚, 吕辉, 李刚.一种BP神经网络的改进方法及其应用.中国工程科学, 2005, 7(5):63-65. http://www.cnki.com.cn/Article/CJFDTOTAL-GCKX200505010.htm
[25]	雷景生, 康耀红.一种改进的BP算法.海南大学学报自然科学, 2003, 21(4):326-329.
[26]	Rumelhart D E, Hinton G E, Williams R J.Learning Inter-nal Representation by Error Propagation.Parallel Distributed Processing:Explorations in the Microstructure of Cognition, 1986 :318-362. https://www.researchgate.net/publication/230876435_Learning_Internal_Representation_by_Error_Propagation
[27]	Lippmann R P.An introduction to computing with neural nets.IEEE ASSP Magazine, 1987, 4 :4-22.
[28]	Hecht-Nielsen R.Theory of the back-propagation neural net-work.IEEE International Joint Conference on Neural Networks, 1989, 1 :593-605. http://industry.wanfangdata.com.cn/dl/Detail/ExternalResource?id=dianzixb201412010%5e14
[29]	Huang S C, Huang Y F.Bounds on number of hidden neu-rons in multilayer perceptrons.IEEE Transactions on Neu-ral Networks, 1991, 2 :47-55. doi: 10.1109/72.80290
[30]	Reed R, Pruning algorithm-a survey.IEEE Transactions on Neural Networks, 1993, 4 :740-747. doi: 10.1109/72.248452