黏粒含量与泥石流容重关系的PPR模型分析研究
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摘要
通过投影寻踪回归分析法研究黏粒含量与泥石流容重之间的关系,结合泥石流动能机理和颗粒悬浮机理进行分析。结果表明:采用PPR单纯建模分析时,PPR模型和多项式模型的平均相对误差分别为5. 3%和6. 5%,在相对误差小于20%、15%、10%和5%时,PPR模型合格率相较于多项式模型分别提高了3. 9%、10. 6%、23%和24%。当采用PPR预留检验分析时,相较于多项式模型,建模分析组和预留检验组的合格率均有所提高。此外,黏粒含量与泥石流容重之间存在非线性相关,当黏粒含量小于6. 78%时,泥石流容重随黏粒含量的增加而增大;当黏粒含量大于6. 78%时,泥石流容重随着黏粒含量的增加而减小;当黏粒含量在6%～10%时,所形成的泥石流平均容重值最大,在2. 05～2. 20 g/cm3波动。PPR模型相较于多项式模型优势显著,其相对误差较小,回归拟合稳定,分析结果与泥石流动能机理和颗粒悬浮机理结论相吻合,进一步说明了PPR模型在兼容性和定量信息利用方面更具优越性。
This paper details the relationship between the content of clay particles and the density of debris flow by projection pursuit regression analysis,and the kinetic mechanism of debris flow and the mechanism of particle suspension were analyzed. The results show that the average relative error of PPR model and polynomial model is 5. 3% and 6. 5%,respectively when the PPR model is simple-modeled,when the relative error is less than 20%,15%,10% and 5%,the rate of the PPR model increases by3. 9%,10. 6%,23% and 24%. When using PPR reservation test analysis,compared with the polynomial model,the qualification rate of the modeling analysis group and the reservation test group was improved.In addition,there is a non-linear correlation between the content of clay particles and the density of debris flow. When the content of clay particles is less than 6. 78%,the density of debris flow increases with the increase of the content of clay particles,and when the content of clay is more than 6. 78%,the density of debris flow decreases with the increase of the content of clay particles. When the clay content is between 6% ～ 10%,the average bulk density of debris flow is the largest and fluctuates between 2. 05 ～2. 20 g/cm3. This paper proves that the PPR model has obvious advantages over the polynomial model,its relative error is smaller,the regression fitting is stable,the analysis result accords with the kinetic en-ergy mechanism of debris flow and the conclusion of particle suspension mechanism,and further illustrates that PPR model has more advantages in compatibility and quantitative information utilization.
This paper details the relationship between the content of clay particles and the density of debris flow by projection pursuit regression analysis,and the kinetic mechanism of debris flow and the mechanism of particle suspension were analyzed. The results show that the average relative error of PPR model and polynomial model is 5. 3% and 6. 5%,respectively when the PPR model is simple-modeled,when the relative error is less than 20%,15%,10% and 5%,the rate of the PPR model increases by3. 9%,10. 6%,23% and 24%. When using PPR reservation test analysis,compared with the polynomial model,the qualification rate of the modeling analysis group and the reservation test group was improved.In addition,there is a non-linear correlation between the content of clay particles and the density of debris flow. When the content of clay particles is less than 6. 78%,the density of debris flow increases with the increase of the content of clay particles,and when the content of clay is more than 6. 78%,the density of debris flow decreases with the increase of the content of clay particles. When the clay content is between 6% ～ 10%,the average bulk density of debris flow is the largest and fluctuates between 2. 05 ～2. 20 g/cm3. This paper proves that the PPR model has obvious advantages over the polynomial model,its relative error is smaller,the regression fitting is stable,the analysis result accords with the kinetic en-ergy mechanism of debris flow and the conclusion of particle suspension mechanism,and further illustrates that PPR model has more advantages in compatibility and quantitative information utilization.
引文
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[12]徐刚,余冲.基于遗传算法的参数投影寻踪回归径流预报模型及应用[J].水电能源科学,2013,31(9):20-23.
[13]吴思,赵涛,拜亚茹,等.不同粗糙壁面人工渠道糙率影响因子试验研究[J].人民黄河,2018,40(1):153-156.
[14]胡桂胜,陈宁生,李俊,等.金沙江白鹤滩水电站坝址近场区泥石流运动特性与发展趋势分析[J].水土保持研究,2014,21(2):238-245.
[15]王兆印,崔鹏,余斌.泥石流的运动机理及减阻[J].自然灾害学报,2001,10(3):37-43.
[16]杨太强,蒋玉,徐则民.怒江东月各泥石流搬运能力研究[J].自然灾害学,2016,25(5):55-63.
[17]王裕宜,费祥俊.粘性泥石流颗粒悬浮机理研究[J].中国科学:E辑,1999,29(4):372-377.
[2]张楠,方志伟,韩笑,等.近年来我国泥石流灾害时空分布规律及成因分析[J].地学前沿,2018,25(2):299-308.
[3]赵海鑫,游勇,柳金峰,等.泥石流流变特性分析[J].科学技术与工程,2015,15(27):120-126.
[4]马鹏辉,彭建兵,朱兴华,等.陕西蓝田铜鹅沟泥石流发育特征及危险性分析[J].中国地质灾害与防治学报,2017,28(1):75-81.
[5]廖彬,常鸣.都江堰市龙池镇李泉太沟泥石流灾害研究[J].南水北调与水利科技,2014,12(2):102-106.
[6]胡赵兴,张开平,马槽.泥石流容重的流动还原实验法研究[C]//2015年海峡两岸水土保持学术研讨会论文集.太原:中国水土保持学会,2015:372-376.
[7]韩征,徐林荣,苏志满,等.基于流域形态完整系数的泥石流容重计算方法[J].水文地质工程地质,2012,39(2):100-105.
[8]陈宁生,崔鹏,刘中港,等.基于黏土颗粒含量的泥石流容重计算[J].中国科学E辑:科学技术,2003,33(增):164-174.
[9]李祚泳,张正健,余春雷.基于投影寻踪回归的指标规范值的水质评价模型[J].水文,2012,32(3):6-12.
[10]任露泉.试验优化设计与分析[M].北京:高等教育出版社,2003.
[11] MAJID FATIMA,OUARDI ABDERAZZAK,BARAKAT MOHAMED,et al. Mechanical behavior prediction of PPR and HDPE polymers through newly developed nonlinear damage-reliability models[J]. Procedia Structural Integrity,2017(3):387-394.
[12]徐刚,余冲.基于遗传算法的参数投影寻踪回归径流预报模型及应用[J].水电能源科学,2013,31(9):20-23.
[13]吴思,赵涛,拜亚茹,等.不同粗糙壁面人工渠道糙率影响因子试验研究[J].人民黄河,2018,40(1):153-156.
[14]胡桂胜,陈宁生,李俊,等.金沙江白鹤滩水电站坝址近场区泥石流运动特性与发展趋势分析[J].水土保持研究,2014,21(2):238-245.
[15]王兆印,崔鹏,余斌.泥石流的运动机理及减阻[J].自然灾害学报,2001,10(3):37-43.
[16]杨太强,蒋玉,徐则民.怒江东月各泥石流搬运能力研究[J].自然灾害学,2016,25(5):55-63.
[17]王裕宜,费祥俊.粘性泥石流颗粒悬浮机理研究[J].中国科学:E辑,1999,29(4):372-377.
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