Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve ...Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.展开更多
针对起重伤害事故致因风险关联性强、节点权重差异大导致风险分级偏离实际的问题,提出了改进型拓扑势风险分级方法。收集2013—2024年381起起重伤害事故报告文本数据,挖掘致因风险的“人-机-环-管”要素;应用Apriori算法,解析致因风险...针对起重伤害事故致因风险关联性强、节点权重差异大导致风险分级偏离实际的问题,提出了改进型拓扑势风险分级方法。收集2013—2024年381起起重伤害事故报告文本数据,挖掘致因风险的“人-机-环-管”要素;应用Apriori算法,解析致因风险的关联关系,构建致因风险拓扑网络;采用作业条件危险性评价法LEC(Likelihood, Exposure and Consequence)厘定致因风险网络的异质节点权重;融合高斯势函数建立拓扑势计算模型,实现风险分级。结果表明:致因风险节点出度拓扑势呈“核心-边缘”分布,管理类因素如主体责任不落实、监管不到位是核心驱动节点;部件故障、违规指挥等是直接诱因;风险划分为Ⅰ、Ⅱ、Ⅲ级,据此提出针对性的防控策略。研究结果为起重伤害事故致因风险分级提供了新的系统性量化工具。展开更多
基金the Ontario Ministry of Agriculture,Food and Rural Affairs,Canada,who supported this project by providing updated soil information on Ontario and Middlesex Countysupported by the Natural Science and Engineering Research Council of Canada(No.RGPIN-2014-4100)。
文摘Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.
文摘针对起重伤害事故致因风险关联性强、节点权重差异大导致风险分级偏离实际的问题,提出了改进型拓扑势风险分级方法。收集2013—2024年381起起重伤害事故报告文本数据,挖掘致因风险的“人-机-环-管”要素;应用Apriori算法,解析致因风险的关联关系,构建致因风险拓扑网络;采用作业条件危险性评价法LEC(Likelihood, Exposure and Consequence)厘定致因风险网络的异质节点权重;融合高斯势函数建立拓扑势计算模型,实现风险分级。结果表明:致因风险节点出度拓扑势呈“核心-边缘”分布,管理类因素如主体责任不落实、监管不到位是核心驱动节点;部件故障、违规指挥等是直接诱因;风险划分为Ⅰ、Ⅱ、Ⅲ级,据此提出针对性的防控策略。研究结果为起重伤害事故致因风险分级提供了新的系统性量化工具。
