面向越野通行分析的土壤数据分类方法研究
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摘要
土壤是影响部队越野机动的重要因素,土壤与气象因素相结合使得越野通行性分析变得异常复杂。基于统一土壤分类系统(Unified Soil Classification System,USCS)的土壤数据是定量分析土壤通行性的基础,采用随机森林方法,利用土壤的多种属性信息来预测土壤的USCS类型。从已有的USCS土壤数据中提取样本数据构建8种随机森林模型,对不同随机森林模型的精度和特征变量的重要性进行分析,根据土壤数据集的特点采用第三种随机森林模型对其进行处理,构建适合越野通行分析的土壤数据。相比以往的方法,这种方法精度更高,更能满足越野通行分析的需要。
Soil is one of the most important factors influencing the off-road maneuver of the army.The combination of soil and weather factors makes cross-country trafficability analysis extremely complicated.The soil data from unified soil classification system(USCS) are the basis for the analysis of soil trafficability.In this paper,the random forest method is used to predict the type of soil by using various attribute information.The method extracts sample data from existing USCS soil data to construct multiple random forest models,then analyses the accuracy of random forests and the importance of characteristic variables,and finally uses the third random forest model to process soil database according to the characteristics of the data.Compared with the previous methods,this method is more accurate and can meet the requirements of cross-country trafficability analysis.
Soil is one of the most important factors influencing the off-road maneuver of the army.The combination of soil and weather factors makes cross-country trafficability analysis extremely complicated.The soil data from unified soil classification system(USCS) are the basis for the analysis of soil trafficability.In this paper,the random forest method is used to predict the type of soil by using various attribute information.The method extracts sample data from existing USCS soil data to construct multiple random forest models,then analyses the accuracy of random forests and the importance of characteristic variables,and finally uses the third random forest model to process soil database according to the characteristics of the data.Compared with the previous methods,this method is more accurate and can meet the requirements of cross-country trafficability analysis.
引文
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[19]贾富仓,李华.基于随机森林的多谱磁共振图像分割[J].计算机工程,2005,31(10):159-161.Jia Fucang,Li Hua.Multi-spectral Magnetic Resonance Image Segmentation Using Random Forests[J].Computer Engineering,2005,31(10):159-161.
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[21]Breiman L.Random Forests[J].Machine Learning(S1573-0565),2001,45(1):5-32.
[22]周志华.机器学习[M].北京:清华大学出版社,2016:171-180.Zhou Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016:171-180.
[2]黄鲁峰.基于GIS的战场自然环境因子综合分析研究-以地形通行性能为例分析[D].郑州:解放军信息工程大学,2008.Huang Lufeng.A Study of the Comprehensive Analysis of Natural Environmental Factors in Battlefield Based on GIS-A Case of Terrain Through Performance Analysis[D].Zhengzhou:PLA Information Engineering University for the Degree of Master of Engineering,2008.
[3]李军,李灏,宁俊帅.履带车辆松软路面通过性分析[J].农业装备与车辆工程,2010(5):3-6.Li Jun,Li hao,Ning Junshuai.Analysis of the Mobility of Tracked Vehicle on Soft Soil[J].Agricultural Equipment&Vehicle engineering,2010(5):3-6.
[4]王国军.美军车辆软地面通过性试验方法问题分析[J].军事交通学院学报,2015,17(3):53-56.Wand Guojun.Analysis of US Army’s Experimental Method of Vehicle Trafficability on Soft Ground[J].Journal of Military Transportation University,2015,17(3):53-56.
[5]林剑辉,孙宇瑞,马道坤.土壤圆锥指数及其测量技术研究进展[J].湖南农业科学,2011(13):155-159.Lin Jianhui,Sun Yurui,Ma Daokun.Research Progress of Soil Cone Index and Its Measurement Technology[J].Hunan agricultural Sciences,2011(13):155-159.
[6]李刚,杨建学,马国宇,等.江苏响水滩涂土壤圆锥指数试验研究[J].汽车工程学报,2012,2(4):292-296.Li Gang,Yang Jianxue,Ma Guoyu,et al.Experimental Study on Soil Cone Index of Xiangshui Beach of Jiangsu Province[J].Chinese Journal of Automotive Engineering,2012,2(4):292-296.
[7]Frankenstein S.Fast All-Season Soil STrength(FASST)[R].Washington:U.S.Army Corps of Engineers Washington,2004:62-74.
[8]张立彬.土壤含水量和土壤紧实度对土壤圆锥指数值影响的试验研究[J].农业工程学报,1993,9(2):41-44.Zhang Libin.Test on Effect of Soil Moisture and Density on Soil Cone Indexes[J].Transaction of the CSAE,1993,9(2):41-44.
[9]林剑辉,孙宇瑞,曾庆猛.土壤圆锥指数、水分与容重耦合模型的试验比较-I.模型性能的统计检验与容重预测[J].应用基础与工程科学学报,2008,16(6):779-786.Lin Jianhui,Sun Yurui,Zeng Qingmeng.Experimental Comparison of Various Models Related Soil Water Content and Bulk Density to Cone Index-I.A General and Statistical Analysis for Each Model[J].Journal of Basic Science and Engineering,2008,16(6):779-786.
[10]Frankenstein S,Koenig G.The Effects of Vegetation on the Dynamic State of the Ground[J].Effects of Vegetation on the Dynamic State of the Ground,2003,128(2):313-317.
[11]ASTM D2487.Standard Practice for Classification of Soils for Engineering Purposes(Unified Soil Classification System)[S].US:ASTM,2000.
[12]Curtis J O.Electromagnetic Power Attenuation in Soils[R].U.S.Army Engineer Research and Development Center,2005.
[13]Ayers P D,G Bozdech,J Freeman,et al.Development of a dynamic visco-elastic vehicle-soil interaction model for rut depth,energy and power determinations[C].17th International Conference of the International Society for Terrain-Vehicle Systems,2011.
[14]Baylot A B,M T Stevens,J A Patterson,et al.Arc of Instability and Combatant Command Terrain Geostatistics and Ground Vehicle Mobility Predictions[R].U.S.Army Engineer Research and Development Center,2014.
[15]Garcia-Gaines R A,Frankenstein S.USCS and the USDAsoil classification system[R].U.S.Army Engineer Research and Development Center,2014.
[16]蔡永明,张科利,李双才.不同粒径制间土壤质地资料的转换问题研究[J].土壤学报,2003,40(4):511-517.Cai Yongming,Zhang Keli,Li Shuangcai.Study on the Conversion of Different Soils Texture[J].Acta Pedologica Sinica,2003,40(4):511-517.
[17]花可可,魏朝富.土壤液限和抗剪强度特征值及其影响因素研究-基于紫色土区[J].农机化研究,2011,33(6):105-110.Hua Keke,Wei Chaofu.Characters and Effects of Soil Liquid Limit and Shear Strength in Purple Hillymountainous Region[J].Agricultural mechanization research,2011,33(6):105-110
[18]张华伟,王明文,甘丽新.基于随机森林的文本分类模型研究[J].山东大学学报理学版,2006,41(3):5-9.Zhang Huawei,Wang Mingwen,Gan Lixin.Automatic text classification model based on random forest[J].Journal of Shandong University,2006,41(3):5-9.
[19]贾富仓,李华.基于随机森林的多谱磁共振图像分割[J].计算机工程,2005,31(10):159-161.Jia Fucang,Li Hua.Multi-spectral Magnetic Resonance Image Segmentation Using Random Forests[J].Computer Engineering,2005,31(10):159-161.
[20]马玥,姜琦刚,孟治国.基于随机森林算法的农耕区土地利用分类研究[J].农业机械学报,2016,47(1):297-303.Ma Yue,Jiang Qigang,Meng Zhiguo.Classification of Land Use in Farming Area Based on Random Forest Algorithm[J].Transaction of the Chinese Society for Agricultural Machinery,2016,47(1):297-303.
[21]Breiman L.Random Forests[J].Machine Learning(S1573-0565),2001,45(1):5-32.
[22]周志华.机器学习[M].北京:清华大学出版社,2016:171-180.Zhou Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016:171-180.