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基于决策树的区域地块风险管控模式预测
引用本文:朱文会,王夏晖,杨欣桐,何俊,卢然,张筝.基于决策树的区域地块风险管控模式预测[J].中国环境科学,2021,41(12):5771-5778.
作者姓名:朱文会  王夏晖  杨欣桐  何俊  卢然  张筝
作者单位:生态环境部环境规划院土壤环境保护中心, 重金属污染防治研究中心, 北京 100012
基金项目:国家重点研发计划(2018YFC1800200;2018YFC1800205)
摘    要:针对传统地块风险管控模式确定存在效率低、成本高、缺乏系统性的缺陷,通过构建区域地块风险管控模式决策的特征数据集,并采用卡方自动交叉检验(CHAID)、穷举卡方自动交叉检验(E-CHAID)、分类与回归树(CART)3种决策树(DT),探索不同DT算法应用于区域地块风险管控模式预测的可行性.结果表明,DT应用于区域地块风险管控模式预测是可行的.CART-DT在准确率(ACC)、精度(PRE)、召回率(REC)、F1值方面的性能均显著优于CHAID-DT和E-CHAID-DT.CART的总体优化算法可能更适合区域地块风险管控模式的预测.区域保护目标(RPG)、区域污染物类型(RPT)、区域企业平均生产年限(RAPP)3项输入变量对CART-DT输出的重要性非常高;区域年平均风速(RAAWS)、区域地形地貌(RT)、区域土地增值潜力(RLVP)等11项输入变量对CART-DT输出的重要性较高;区域人口密度(RPD)、区域主导行业风险(RDIS)等6项输入变量对CART-DT的输出也有一定贡献.

关 键 词:决策树(DT)  区域地块  风险管控模式  
收稿时间:2021-04-09

Prediction performance of risk management and control mode in regional sites based on decision tree
ZHU Wen-hui,WANG Xia-hui,YANG Xin-tong,HE Jun,LU Ran,ZHANG Zheng.Prediction performance of risk management and control mode in regional sites based on decision tree[J].China Environmental Science,2021,41(12):5771-5778.
Authors:ZHU Wen-hui  WANG Xia-hui  YANG Xin-tong  HE Jun  LU Ran  ZHANG Zheng
Institution:Soil Environmental Protection Center, Research Center of Heavy Metal Pollution Prevention and Control, Chinese Academy for Environmental Planning, Beijing 100012, China
Abstract:In order to overcome the defects of low efficiency, high cost and lack of systematicness, which had been found in screening of traditional risk management and control mode in regional sites. Three decision tree (DT) algorithms, including Chi-squared Automatic Interaction Detector (CHAID), Exhaustive CHAID (E-CHAID), and Classification And Regression Tree (CART), were implemented to predict risk management and control mode in regional sites. Characteristic data set was constructed using existing risk management and control mode of regional sites and relevant regional attributes. The results showed that, DT-based mode could be used to predict risk management and control mode in regional sites. In the aspects of accuracy (ACC), precision (PRE), recall ratio (REC) and F1 value, the performance of CART-DT was superior to CHAID-DT and E-CHAID-DT. The overall optimization algorithm of CART might be more suitable for predicting risk management and control mode in regional sites. Regional protection goal (RPG), regional pollution type (RPT) and regional average production period (RAPP) were the three dominant factors of CART-DT output. Eleven input variables such as regional average annual wind speed (RAAWS), regional topography (RT) and regional land value-added potential (RLVP) were relatively important to CART-DT output. Regional population density (RPD), regional dominant industry risk (RDIS) and four other input variables had relatively low impact on CART-DT output.
Keywords:decision tree (DT)  regional site  risk management and control mode  
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