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Bayesian networks and GIS techniques for modelling the causality, intensity and extent of land degradation in drylands.

机译：利用贝叶斯网络和GIS技术对干旱地区土地退化的因果关系，强度和程度进行建模。

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摘要

In this thesis, a new probabilistic approach to assess land degradation and its causes in dry lands is introduced. The suitability of Bayesian Networks for modelling the causality of land degradation intensity and extent through the integration of driving forces, pressures, states impacts and responses (DPSIR) is evaluated. In an attempt to describe the relationships between bio-physical states of degradation to their social, economic and demographic causes, the proposed DPSIR framework offers a new probabilistic approach to the establishment of the major root causes of the states of degradation in a study area, resulting in a practical Bayesian network modelling application and implementation to land degradation data.; A Bayesian network model has been constructed and tested using DPSIR indicators of land degradation in El Alegre watershed, San Luis Potosi, Mexico, using data obtained from measurements recorded in field forms and questionnaires applied during interviews with farmers and herders and local experts and officials. These data were used as input to the model developed using Netica(TM) software.; The Bayesian network model was developed by linking indicators of Drivers and Pressures to State indicators based on their presumed cause-effect relationships. These relationships were derived from expert knowledge and available combination of data sources in the study area. Values (intensity or extent) of status were assigned to each degradation indicator based on all combinations of the status (intensities or extents) of each of its identified causes. The final built model enables the visualization of the causality, intensity, and extent (of coverage over the area) of each indicator (drivers, pressures and states) within the model and to identify the most probable causes (drivers and pressures) of each of the state Indicators of land degradation from the sensitivity analysis of the model. This determines the most influencing causes for each indicator of the state of degradation. The causal relationships predicted by the model were validated independently through a confusion matrix and the Cohen's Kappa technique using local farmers' perceptions of the causes for a given type of degradation collected from interviews in the field through questionnaires. The results showed that the agreement between farmer perceptions of causes for each degradation state and the predicted causes by the model was good, but modest. This modest agreement was attributed, to a large extent, to the degree of subjectivity involved in interpreting vague farmer responses in the questionnaires. However the causality model proved empirically accurate according to the knowledge of local experts. Finally, using GIS the results of the present states of degradation of such dry lands, and their causes (drivers and pressures) were mapped coding each degradation indicator in an ad-hoc map legend, including their intensity, spatial extent and most influencing causes.
机译：本文提出了一种评估干旱地区土地退化及其成因的新概率方法。评估了贝叶斯网络通过综合驱动力，压力，状态影响和响应（DPSIR）建模土地退化强度和程度的因果关系的适用性。为了描述退化的生物物理状态与其社会，经济和人口统计学原因之间的关系，提议的DPSIR框架提供了一种新的概率方法来确定研究区域中退化状态的主要根源，导致针对土地退化数据的实用贝叶斯网络建模应用和实现；已经建立了贝叶斯网络模型，并使用DPSIR指标在墨西哥圣路易斯波托西的El Alegre流域进行了土地退化，并使用了田间表格中记录的测量数据以及在与农民和牧民以及当地专家和官员的访谈中使用的问卷调查中获得的数据。这些数据被用作使用Netica TM软件开发的模型的输入。贝叶斯网络模型是通过将驱动力和压力指标与状态指标基于其假定的因果关系联系起来而开发的。这些关系来自专家知识以及研究区域中数据源的可用组合。基于每个已识别原因的状态（强度或程度）的所有组合，将状态值（强度或程度）分配给每个降级指标。通过最终构建的模型，可以可视化模型中每个指标（驱动因素，压力和状态）的因果关系，强度和程度（覆盖范围），并确定每个因素的最可能原因（驱动因素和压力）通过模型的敏感性分析得出土地退化的状态指标。这确定了每种降解状态指标的最大影响原因。该模型所预测的因果关系通过混淆矩阵和Cohen's Kappa技术进行了独立验证，其中使用了当地农民对通过问卷调查从田间访谈中收集的给定类型的退化原因的理解。结果表明，农民对每种退化状态的原因感知与模型预测的原因之间的一致性很好，但适度。这种谦虚的协议在很大程度上归因于解释问卷中模糊的农民回答所涉及的主观程度。但是，根据当地专家的知识，因果关系模型在经验上证明是准确的。最后，使用GIS，将这些干旱土地退化的现状及其原因（驱动因素和压力）的结果进行了编码，并在临时图例中对每个退化指标进行了编码，包括其强度，空间范围和影响最大的原因。

著录项

  • 作者

    Ahmed, Oumer.;

  • 作者单位

    Trent University (Canada).;

  • 授予单位 Trent University (Canada).;
  • 学科 Environmental Sciences.
  • 学位 M.Sc.
  • 年度 2008
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 环境科学基础理论;
  • 关键词

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