设为首页
加入收藏
Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement
-
摘要: 利用福建省古田人工增雨试验基地2014年1月—2023年1月小时自然降水数据, 结合线性拟合、多项式回归和样条回归等多种数学统计方法, 开展决策树、支持向量机(SVM)和卷积神经网络(CNN)3种机器学习方法在估测目标区自然降水中的应用研究。目标区和对比区自然雨量关系模型对比结果表明:以区域平均面雨量为统计变量时, CNN和四项式回归效果相对较好, 其中CNN的确定系数为0.516, 均方根误差为1.097 mm;对平均面雨量进行六次方根变换后, 各模型的精准度大幅提升, CNN表现最优, 确定系数为0.658, 其次为SVM;为克服目标区和对比区雨量时间序列效应及空间分布不均等问题, 以面雨量空间格点数据作为研究对象, 采用CNN 3种优化器(自适应矩估计、均方根传递和梯度随机下降)算法进行对比, 发现基于自适应矩估计优化器建立目标区和对比区雨量关系模型最优, 其降水估测值与实测值更接近, 均方根误差最小, 为0.61 mm。因此, 利用CNN方法能够进一步优化目标区和对比区雨量关系模型, 可为定量评估人工增雨效果提供参考。Abstract: As an important part of weather modification operation, the scientific effectiveness evaluation of artificial rainfall enhancement has gradually attracted attentions of government and public. In order to evaluate effects of artificial rainfall enhancement objectively and quantitatively, combing linear fitting, polynomial regression, spline regression and 3 other machine learning methods including decision tree, support vector machine and neural network, the relationship model between the rainfall in the target area and the contrast area is established based on rainfall data and operation information of recent 10 years in Fujian. Different historical regression statistical test schemes of rainfall enhancement effects are compared and analyzed, aiming to further optimize the best natural rainfall estimation algorithm based on alterable contrast area with statistical method, which can provide reference for the assessment of artificial rainfall enhancement effects. Results show that historical rainfall data samples are mainly concentrated in the weak rainfall grade. Using multiple regression methods (linear regression, polynomial regression and spline regression), the piecewise statistics of rainfall data does not significantly improve the linear regression model between two regions, and its root mean square error (RMSE) is generally higher than the statistical results. By comparing various machine learning and linear regression models, it is found that CNN and quomial regression perform relatively well when the regional average surface rainfall is taken as the statistical variable, with the determination coefficient of CNN being 0.516 and RMSE being 1.097 mm. Each statistical model is greatly improved after six root square transformations of rainfall data. The performance of the model established by CNN method is relatively optimal, with determination coefficient R2 up to 0.658 and RMSE only
-
图 1 福建省古田人工增雨效果检验基地
Fig. 1 Gutian artificial rainfall effect evaluation base in Fujian
图 2 目标区与对比区地形相似度与降水相似度拟合关系
Fig. 2 Fitting relationship between topography similarity and rainfall correlation coefficient in target area and contrast area
图 3 古田随机试验目标区、浮动对比区与最佳对比区设置
Fig. 3 Artificial rainfall target area, floating contrast area and the best-contrast area of Gutian
图 5 不同雨强下各模型的降水估测均方根误差对比
Fig. 5 Root mean square error of rainfall estimation for different rainfall intensity by different models
图 6 基于总样本的不同模型降水估测的均方根误差和确定系数
Fig. 6 Root mean square error and determination coefficient of rainfall estimation based on total samples by different models
图 7 目标区和对比区格点化及区域自动气象站分布
Fig. 7 Regional grid and automatic weather station in target area and contrast area
图 8 2016年4月9日19:00不同插值方法得到的面雨量分布
Fig. 8 Area rainfall distribution with different methods at 1900 BT 9 Apr 2016
图 9 3种优化器降水估测对比
Fig. 9 Comparison of rainfall estimation among 3 machine learning optimizers
表 1 2014—2023年不同等级降水样本量
Table 1 Sample size of different rainfall categories in 2014-2023
降水等级 雨强/(mm·h-1) 样本量 样本比例/% 弱降水 [0.1, 5) 25207 95.98 一般降水 [5, 10) 780 2.97 中等降水 [10, 25) 269 1.02 强降水 [25, +∞) 7 0.03 
下载: 导出CSV
-
[1] 毛节泰, 郑国光.对人工影响天气若干问题的探讨.应用气象学报, 2006, 17(5):643-646. doi: 10.3969/j.issn.1001-7313.2006.05.015Mao J T, Zheng G G. Discussions on some weather modification issues. J Appl Meteor Sci, 2006, 17(5): 643-646. doi: 10.3969/j.issn.1001-7313.2006.05.015 [2] 郭学良, 方春刚, 卢广献, 等. 2008—2018年我国人工影响天气技术及应用进展. 应用气象学报, 2019, 30(6): 641-650. doi: 10.11898/1001-7313.20190601Guo X L, Fang C G, Lu G X, et al. Progresses of weather modification technologies and applications in China from 2008 to 2018. J Appl Meteor Sci, 2019, 30(6): 641-650. doi: 10.11898/1001-7313.20190601 [3] 姚展予. 中国气象科学研究院人工影响天气研究进展回顾. 应用气象学报, 2006, 17(6): 786-795. doi: 10.3969/j.issn.1001-7313.2006.06.016Yao Z Y. Review of weather modification research in Chinese Academy of Meteorological Sciences. J Appl Meteor Sci, 2006, 17(6): 786-795. doi: 10.3969/j.issn.1001-7313.2006.06.016 [4] 叶家东, 范蓓芬. 人工影响天气的统计数学方法. 北京: 科学出版社, 1982.Ye J D, Fan B F. Statistical and Mathematical Methods of Weather Modification. Beijing: Science Press, 1982. [5] 叶家东, 范蓓芬, 杜京朝. 人工增雨试验中的反效果问题. 应用气象学报, 1998, 9(3): 336-344. http://qikan.camscma.cn/article/id/19980348Ye J D, Fan B F, Du J C. Study of negative effects in artificial precipitation enhancement experiments. J Appl Meteor Sci, 1998, 9(3): 336-344. http://qikan.camscma.cn/article/id/19980348 [6] 曾光平, 方仕珍. 福建省古田水库人工降雨试验效果的多元回归分析. 热带气象, 1986, 2(4): 336-342.Zeng G P, Fang S Z. The result multivariate analysis of artificial rainfall in Fujian Gutian Area during 1975-1984. J Trop Meteor, 1986, 2(4): 336-342. [7] 曾光平, 方仕珍, 肖锋. 1975—1986年古田水库人工降雨效果总分析. 大气科学, 1991, 15(4): 97-108. doi: 10.3878/j.issn.1006-9895.1991.04.11Zeng G P, Fang S Z, Xiao F. The total analysis of the effect of artificial rainfall in Gutian Reservoir Area, Fujian(1975-1986). Chinese J Atmos Sci, 1991, 15(4): 97-108. doi: 10.3878/j.issn.1006-9895.1991.04.11 [8] 曾光平, 吴明林, 林长城, 等. 古田水库人工降雨效果的综合评价. 应用气象学报, 1993, 4(2): 154-161. http://qikan.camscma.cn/article/id/19930229Zeng G P, Wu M L, Lin C C, et al. A comprehensive evaluation of the effect of artificial precipitation in Gutian Reservior Area. J Appl Meteor Sci, 1993, 4(2): 154-161. http://qikan.camscma.cn/article/id/19930229 [9] 曾光平. 非随机化人工增雨试验效果的统计模拟研究. 应用气象学报, 1999, 10(2): 255-256. doi: 10.3969/j.issn.1001-7313.1999.02.017Zeng G P. Statistical simulation study on the effect of non-randomized artificial precipitation enhancement experiment. J Appl Meteor Sci, 1999, 10(2): 255-256. doi: 10.3969/j.issn.1001-7313.1999.02.017 [10] 曾光平, 张长安, 李茂仑. 人工降水方案统计设计的统计数值模拟方法研究. 大气科学, 2000, 24(1): 131-141.Zeng G P, Zhang C A, Li M L. Study on statistic numerical simulation method of precept statistic design of artificial precipitation. Chinese J Atmos Sci, 2000, 24(1): 131-141. [11] 蒋年冲, 吴林林, 曾光平. 抗旱型火箭人工增雨效果检验方法初步研究. 气象, 2006, 32(8): 54-58.Jiang N C, Wu L L, Zeng G P. On effect test of drought-resistant rocketry artificial precipitation enhancement operation. Meteor Mon, 2006, 32(8): 54-58. [12] 王以琳, 李德生, 刘诗军. 飞机人工增雨分层历史回归效果检验方法探讨. 气候与环境研究, 2012, 17(6): 862-870.Wang Y L, Li D S, Liu S J. Stratified sampling historical regression method for aircraft precipitation enhancement effect test. Climate Environ Res, 2012, 17(6): 862-870. [13] 王婉, 石玉恒, 李宏宇, 等. 对流云人工增雨效果检验技术方法及应用. 气象科技, 2014, 42(6): 1131-1136. doi: 10.3969/j.issn.1671-6345.2014.06.031Wang W, Shi Y H, Li H Y, et al. A method for evaluating effectiveness of convective cloud precipitation enhancement and its application. Meteor Sci Technol, 2014, 42(6): 1131-1136. doi: 10.3969/j.issn.1671-6345.2014.06.031 [14] 贾烁, 姚展予. 江淮对流云人工增雨作业效果检验个例分析. 气象, 2016, 42(2): 238-245.Jia S, Yao Z Y. Case study on the convective clouds seeding effects in Yangtze-Huaihe Region. Meteor Mon, 2016, 42(2): 238-245. [15] 唐仁茂, 袁正腾, 向玉春, 等. 依据雷达回波自动选取对比云进行人工增雨效果检验的方法. 气象, 2010, 36(4): 96-100. doi: 10.3969/j.issn.1673-8411.2010.04.028Tang R M, Yuan Z T, Xiang Y C, et al. A method for selecting contrast cloud automatically based on radar echo in effectiveness evaluation of rain enhancement. Meteor Mon, 2010, 36(4): 96-100. doi: 10.3969/j.issn.1673-8411.2010.04.028 [16] 王以琳, 姚展予, 林长城. 人工增雨作业前后不同高度雷达回波分析. 干旱气象, 2018, 36(4): 644-651.Wang Y L, Yao Z Y, Lin C C. Analysis of radar echoes at different heights before and after precipitation enhancement. J Arid Meteor, 2018, 36(4): 644-651. [17] 刘晴, 姚展予. 飞机增雨作业物理检验方法探究及个例分析. 气象, 2013, 39(10): 1359-1368. doi: 10.7519/j.issn.1000-0526.2013.10.015Liu Q, Yao Z Y. On physical eveluation of aircraft cloud seeding and case study. Meteor Mon, 2013, 39(10): 1359-1368. doi: 10.7519/j.issn.1000-0526.2013.10.015 [18] 胡淑萍, 林文, 林长城, 等. 2014—2022年古田人工增雨随机试验物理检验. 应用气象学报, 2023, 34(6): 706-716. doi: 10.11898/1001-7313.20230606Hu S P, Lin W, Lin C C, et al. Physical inspection of randomized trial for the artificial rain enhancement experiment at Gutian from 2014 to 2022. J Appl Meteor Sci, 2023, 34(6): 706-716. doi: 10.11898/1001-7313.20230606 [19] 楼小凤, 傅瑜, 孙晶. 一次浙江对流云催化数值模拟试验. 应用气象学报, 2019, 30(6): 665-676. doi: 10.11898/1001-7313.20190603Lou X F, Fu Y, Sun J. A numerical seeding simulation of convective precipitation in Zhejiang, China. J Appl Meteor Sci, 2019, 30(6): 665-676. doi: 10.11898/1001-7313.20190603 [20] 洪延超, 周非非. "催化-供给"云降水形成机理的数值模拟研究. 大气科学, 2005, 29(6): 885-896. doi: 10.3878/j.issn.1006-9895.2005.06.05Hong Y C, Zhou F F. A numerical simulation study of precipitation formation mechanism of "seeding-feeding" cloud system. Chinese J Atmos Sci, 2005, 29(6): 885-896. doi: 10.3878/j.issn.1006-9895.2005.06.05 [21] 龚佃利, 王俊, 刘诗军. 山东降水云系微物理结构数值模拟和播云条件分析. 高原气象, 2006, 25(4): 723-730. doi: 10.3321/j.issn:1000-0534.2006.04.022Gong D L, Wang J, Liu S J. Numerical simulation of cloud microphysical structure and artificial seeding condition in precipitation cloud in Shandong Province. Plateau Meteor, 2006, 25(4): 723-730. doi: 10.3321/j.issn:1000-0534.2006.04.022 [22] 王婉, 姚展予. 2006年北京市人工增雨作业效果统计分析. 高原气象, 2009, 28(1): 195-202.Wang W, Yao Z Y. Statistical estimation of artificial precipitation enhancement effectiveness in Beijing in 2006. Plateau Meteor, 2009, 28(1): 195-202. [23] 汪玲, 韦增岸, 程鹏, 等. 湖南人工增雨作业效果统计检验与分析. 气象研究与应用, 2019, 40(3): 85-89. doi: 10.3969/j.issn.1673-8411.2019.03.020Wang L, Wei Z A, Cheng P, et al. Statistical tests and analysis of effective evaluation of artificial precipitation enhancement operation of Hunan. J Meteor Res Appl, 2019, 40(3): 85-89. doi: 10.3969/j.issn.1673-8411.2019.03.020 [24] 刘晴. 人工增雨效果统计检验方案优选及个例分析. 北京: 中国气象科学研究院, 2013.Liu Q. The Statistical Method Optimization and Case Study of Effectiveness Test in Precipitation Enhancement. Beijing: Academy of Meteorological Sciences, 2013. [25] 程鹏, 陈祺, 蒋友严, 等. 河西走廊石羊河流域近10年人工增雨效果检验评估. 高原气象, 2021, 40(4): 866-874.Cheng P, Chen Q, Jiang Y Y, et al. Effect evaluation of artificial rainfall enhancement in the Shiyang River Basin of Hexi Corridor in the latest 10 years. Plateau Meteor, 2021, 40(4): 866-874. [26] 王飞, 李集明, 姚展予, 等. 我国人工增雨作业效果定量评估研究综述. 气象, 2022, 48(8): 945-962.Wang F, Li J M, Yao Z Y, et al. Advances of quantitative evaluation studies of artificial precipitation enhancement in China. Meteor Mon, 2022, 48(8): 945-962. [27] 王婉, 姚展予. 人工增雨统计检验结果准确度分析. 气象科技, 2009, 37(2): 209-215. doi: 10.3969/j.issn.1671-6345.2009.02.018Wang W, Yao Z Y. Accuracy analysis of statistical evaluation result in precipitation enhancement experiment. Meteor Sci Technol, 2009, 37(2): 209-215. doi: 10.3969/j.issn.1671-6345.2009.02.018 [28] 叶家东, 李铁林. 区域趋势控制协变量回归分析效果评估方法研究. 气象科学, 2001, 21(1): 64-72.Ye J D, Li T L. Evaluation methods of cloud seeding effect with regional control and covariable regression analysis. Sci Meteor Sinica, 2001, 21(1): 64-72. [29] 吴香华, 牛生杰, 金德镇, 等. 自然降水变异对人工增雨效果评估的影响. 中国科学(地球科学), 2015, 45(7): 1011-1019.Wu X H, Niu S J, Jin D Z, et al. Influence of natural rainfall variability on the evaluation of artificial precipitation enhancement. Sci China(Earth Sci), 2015, 45(7): 1011-1019. [30] 房彬, 肖辉, 班显秀. CA-FCM方案与其它几种人工增雨评估方案的比较. 气象科技, 2008, 36(5): 612-621. doi: 10.3969/j.issn.1671-6345.2008.05.021Fang B, Xiao H, Ban X X. Comparison between CA-FCM and some other methods for evaluating precipitation enhancement effectiveness. Meteor Sci Technol, 2008, 36(5): 612-621. doi: 10.3969/j.issn.1671-6345.2008.05.021 [31] 房彬, 肖辉, 王振会, 等. 聚类分析在人工增雨效果检验中的应用. 南京气象学院学报, 2005, 28(6): 739-745. doi: 10.3969/j.issn.1674-7097.2005.06.003Fang B, Xiao H, Wang Z H, et al. Application of cluster analysis to the statistical assessment of the effect of artifical rain enhancement. J Nanjing Inst Meteor, 2005, 28(6): 739-745. doi: 10.3969/j.issn.1674-7097.2005.06.003 [32] 翟羽, 肖辉, 杜秉玉, 等. 聚类统计检验在人工增雨效果检验中的应用. 南京气象学院学报, 2008, 31(2): 228-233. doi: 10.3969/j.issn.1674-7097.2008.02.012Zhai Y, Xiao H, Du B Y, et al. Application of the cluster statistical test to effectiveness evaluation of artificial precipitation enhancement. J Nanjing Inst Meteor, 2008, 31(2): 228-233. doi: 10.3969/j.issn.1674-7097.2008.02.012 [33] 胡莹莹, 庞林, 王启光. 基于深度学习的7~15 d温度格点预报偏差订正. 应用气象学报, 2023, 34(4): 426-437. doi: 10.11898/1001-7313.20230404Hu Y Y, Pang L, Wang Q G. Application of deep learning bias correction method to temperature grid forecast of 7-15 days. J Appl Meteor Sci, 2023, 34(4): 426-437. doi: 10.11898/1001-7313.20230404 [34] 米前川, 高西宁, 李玥, 等. 深度学习方法在干旱预测中的应用. 应用气象学报, 2022, 33(1): 104-114. doi: 10.11898/1001-7313.20220109Mi Q C, Gao X N, Li Y, et al. Application of deep learning method to drought prediction. J Appl Meteor Sci, 2022, 33(1): 104-114. doi: 10.11898/1001-7313.20220109 [35] 刘海知, 徐辉, 包红军, 等. 机器学习分类算法在降雨型滑坡预报中的应用. 应用气象学报, 2022, 33(3): 282-292. doi: 10.11898/1001-7313.20220303Liu H Z, Xu H, Bao H J, et al. Application of machine learning classification algorithm to precipitation-induced landslides forecasting. J Appl Meteor Sci, 2022, 33(3): 282-292. doi: 10.11898/1001-7313.20220303 [36] 兰宇, 罗聪, 伍志方, 等. 三种机器学习方法在广东雷暴大风自动识别的应用效果评估. 热带气象学报, 2023, 39(2): 256-266.Lan Y, Luo C, Wu Z F, et al. The assessment of application effectiveness of three machine learning methods in automatic identification of thunderstorm gale in Guangdong. J Trop Meteor, 2023, 39(2): 256-266. [37] 尹晓燕, 胡志群, 郑佳锋, 等. 利用深度学习填补双偏振雷达回波遮挡. 应用气象学报, 2022, 33(5): 581-593. doi: 10.11898/1001-7313.20220506Yin X Y, Hu Z Q, Zheng J F, et al. Filling in the dual polarization radar echo occlusion based on deep learning. J Appl Meteor Sci, 2022, 33(5): 581-593. doi: 10.11898/1001-7313.20220506 [38] 王星. 大数据分析: 方法与应用. 北京: 清华大学出版社, 2013.Wang X. Big Data Analysis: Methods and Applications. Beijing: Tsinghua University Press, 2013. [39] 胡志晋. 检验人工降水效果的协变量统计分析方法. 气象, 1979, 5(9): 31-33.Hu Z J. Covariance statistical analysis method for testing the effect of artificial precipitation. Meteor Mon, 1979, 5(9): 31-33. [40] 林长城, 姚展予, 林文, 等. 福建省古田试验区云系回波特征与人工增雨作业条件分析. 大气科学学报, 2017, 40(1): 138-144.Lin C C, Yao Z Y, Lin W, et al. Analysis on cloud echoes characteristics and operational conditions of precipitation enhancement in Gutian of Fujian. Trans Atmos Sci, 2017, 40(1): 138-144. [41] 王星, 褚挺进. 非参数统计(第2版). 北京: 清华大学出版社, 2014.Wang X, Chu T J. Non-parametric Statistics(2nd ed). Beijing: Tsinghua University Press, 2014. [42] 王衡军. 机器学习: Python sklearn TensorFlow 2.0微课视频版. 北京: 清华大学出版社, 2020.Wang H J. Machine Learning: Python Sklearn Tensor Flow 2.0 Micro-Lesson Video Version. Beijing: Tsinghua University Press, 2020. -
图(9) / 表(1)
计量
- 摘要浏览量: 655
- HTML全文浏览量: 115
- PDF下载量: 141
- 被引次数: 0
