【摘要】 为揭示环境因素与小麦Cd超标风险的关系,综合考虑了小麦Cd富集的7个影响因素(土壤Cd浓度、污染企业、城镇村及工矿用地、交通运输用地、土壤类型、土壤有机质含量(SOM)和土壤pH值),采用ID3算法与朴素贝叶斯算法,建立起5棵贝叶斯决策树.提出了15条小麦Cd超标风险的识别规则,将超标风险分为5级并确定了小麦Cd富集的3个主控因子:污染企业、土壤pH值和土壤Cd浓度.经检验,5棵决策树风险识别的平均精度为81.14%,而使用风险识别规则和贝叶斯算法后识别精度提高为89.32%.该模型将贝叶斯算法融入到了决策树模型,可以评估数据完整或缺失样本的Cd污染风险,确定小麦Cd富集的主控因子,同时可以基于风险识别规则判定小麦Cd风险程度和范围,为土壤安全利用和小麦安全生产区的划定提供参考.更多还原

【Abstract】 In order to reveal the relationship between the environmental factors and the risk of wheat excessive Cd, seven factors(concentration of Cd in soil, polluting enterprises, the town and industrial land, transportation land, soil type, SOM and soil pH) are considered, and five Bayesian decision trees were established based on ID3 algorithm and Naive Bayesian algorithm. 15 identification rules of wheat Cd pollution risk were extracted, and the risk was divided into five levels. Polluting enterprises, soil p H and concentration of Cd in soil were the three dominant factors of wheat Cd enrichment. According to the validation, the average prediction accuracy was 81.14%, and the overall recognition accuracy was improved to 89.32% after using the Bayesian algorithm and risk identification rules. The model integrated the Bayesian algorithm into the decision tree model, which could assess the Cd pollution risk in samples with complete or missing data, determine the dominant factors of wheat Cd enrichment, and identify the degree and region of wheat Cd pollution based on the risk identification rules. This approach could provide a scientific tool for soil safety use and the delimitation of wheat safety production area.更多还原