| 基于决策树的土壤Zn含量预测 |
| 摘要点击 2889 全文点击 1737 投稿时间：2007-12-27 修订日期：2008-01-29 |
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| 中文关键词 决策树 预测 Zn含量 |
| 英文关键词 decision tree predication soil Zn content |
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| 中文摘要 |
| 以浙江省富阳市为研究区域，基于184个土壤表层(0～20 cm)样点的Zn浓度数据（根据背景值划分为G1、G2、G3、G4和G5共5个层次），并结合土壤类型、pH值、有机质、农业用地方式、工矿企业类型、道路和农村居民点等环境因子，采用CART方法挖掘Zn在土壤中的累积规则.利用获得的规则预测剩余41个土壤样点的Zn浓度，并进行精度评价.结果表明，采用CART方法获得的结果比普通Kriging插值方法获得的结果总精度提高了21.95%，在G2和G5层次上模拟精度相差不大，但在G1、G3和G4层次上前者明显高于后者.研究还表明，工矿企业类型在区分土壤Zn含量高低(G1、G2和G3、G4、G5)层次上起主要作用，G1和G2之间，G3、G4和G5之间的土壤Zn含量与pH值、土壤类型和土地利用方式有关. |
| 英文摘要 |
| Taking Fuyang county of Zhejiang Province as the study area，the present study estimated soil Zn concentration (divided by its local background value into G1，G2，G3，G4 and G5) using CART methods，based on 184 soil samples(0-20 cm).The environmental factors used to infer the Zn concentration rules included soil types，pH，organic matter，agricultural land uses，industry plant types，road and village density.The other 41 soil samples were used to test the prediction results.The Zn concentration classes estimated by CART have accuracy in attribution to the right classes of 80.49%.This is a 21.95% improvement on Zn classes estimated by ordinary Kriging method.Concretely，it improved the precision much for G1，G3 and G4，while obtained similar precision for G2 and G5.Moreover，CART provided some insights into the sources of current soil Zn contents.The categories of industrial plants play the most important role in separating the high and low level of Zn concentration (G1,G2 and G3，G4,G5)，and the pH value，soil types and agricultural types play important roles in differentiation among G1 and G2，and among G3，G4 and G5. |
