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Decision-Tree-based data mining and rule induction for predicting and mapping soil bacterial diversity

机译：基于决策树的数据挖掘和规则归纳，用于预测和绘制土壤细菌多样性

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摘要

Soil microbial ecology plays a significant role in global ecosystems. Nevertheless, methods of model prediction and mapping have yet to be established for soil microbial ecology. The present study was undertaken to develop an artificial-intelligence- and geographical information system (GlS)-integrated framework for predicting and mapping soil bacterial diversity using pre-existing environmental geospatial database information, and to further evaluate the applicability of soil bacterial diversity mapping for planning construction of eco-friendly roads. Using a stratified random sampling, soil bacterial diversity was measured in 196 soil samples in a forest area where construction of an eco-friendly road was planned. Model accuracy, coherence analyses, and tree analysis were systematically performed, and four-class discretized decision tree (DT) with ordinary pair-wise partitioning (OPP) was selected as the optimal model among tested five DT model variants. GIS-based simulations of the optimal DT model with varying weights assigned to soil ecological quality showed that the inclusion of soil ecology in environmental components, which are considered in environmental impact assessment, significantly affects the spatial distributions of overall environmental quality values as well as the determination of an environmentally optimized road route. This work suggests a guideline to use systematic accuracy, coherence, and tree analyses in selecting an optimal DT model from multiple candidate model variants, and demonstrates the applicability of the OPP-improved DT integrated with GIS in rule induction for mapping bacterial diversity. These findings also provide implication on the significance of soil microbial ecology in environmental impact assessment and eco-friendly construction planning.%School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea;School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea;School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea;School of Information and Industrial Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, South Korea;School of Information and Industrial Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, South Korea;School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea,WCU Center for Green Metagenomics, Yonsei University, Seoul, Republic of Korea;
机译：土壤微生物生态学在全球生态系统中发挥着重要作用。然而，尚未建立用于土壤微生物生态学的模型预测和作图方法。本研究的目的是开发一个使用人工智能和地理信息系统（GlS）集成的框架，以使用现有的环境地理空间数据库信息来预测和绘制土壤细菌多样性，并进一步评估土壤细菌多样性绘制的适用性。规划建设环保道路。使用分层随机抽样，在计划建设环保道路的林区中，对196个土壤样品中的土壤细菌多样性进行了测量。系统地执行了模型准确性，相干性分析和树分析，并在测试的五个DT模型变体中选择了具有普通成对划分（OPP）的四类离散决策树（DT）作为最佳模型。基于GIS的最佳DT模型的模拟（权重分配给土壤生态质量的权重）表明，在环境影响评估中考虑将土壤生态包含在环境成分中，会对整体环境质量值的空间分布以及总体环境质量产生影响。确定一条环境优化的道路。这项工作提出了使用系统准确性，连贯性和树分析来从多个候选模型变体中选择最佳DT模型的指南，并证明了与GIS集成的OPP改进DT在规则归纳中用于绘制细菌多样性的适用性。这些发现还暗示了土壤微生物生态学在环境影响评估和生态友好型建设规划中的重要性。％延世大学土木与环境工程学院，首尔市西大门区城山路262号，韩国120-749 ；延世大学土木与环境工程学院，大韩民国首尔市西大门区城山路262号，韩国120-749；延世大学土木与环境工程学院，首尔市西大门区，城南山262号，首尔，120-749韩国;延世大学信息与工业工程学院，首尔市西大门区城山路262号，韩国120-749;延世大学信息与工业工程学院，首尔市西大门区，城山市262号韩国120-749；延世大学土木与环境工程学院，首尔市西大门区城山野262号，韩国120-749，延世大学WCU绿色元基因组学中心，首尔，雷普韩国公国

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  • 来源
    《Environmental Monitoring and Assessment》 |2011年第4期|p.595-610|共16页
  • 作者

    Kangsuk Kim; Keunje Yoo; Dongwon Ki; Il Suh Son; Kyong Joo Oh; Joonhong Park;

  • 作者单位

    School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea;

    School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea;

    School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea;

    School of Information and Industrial Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, South Korea;

    School of Information and Industrial Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, South Korea;

    School of Civil and Environmental Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, Republic of Korea,WCU Center for Green Metagenomics, Yonsei University, Seoul, Republic of Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    decision tree; soil microbial ecology; bacterial diversity; artificial intelligence; environmental impact assessment; GIS;

    机译：决策树;土壤微生物生态学细菌多样性人工智能;环境影响评估;地理信息系统;

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