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首页> 外文期刊>The Science of the Total Environment >Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naive Bayes tree for landslide susceptibility modeling
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Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naive Bayes tree for landslide susceptibility modeling

机译：基于GIS的最佳优先决策树，随机森林和朴素贝叶斯树的数据挖掘技术对滑坡敏感性建模的性能评估

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摘要

The main aim of the present study is to explore and compare three state-of-the art data mining techniques, best-first decision tree, randomforest, and naive Bayes tree, for landslide susceptibility assessment in the Longhai area of China. First, a landslide inventory map with 93 landslide locations was randomly divided, with 70% of the area used for training landslide models and 30% used for the validation process. A spatial database of 14 conditioning factors was constructed under a geographic information system environment. Subsequently, the ReliefF method was employed to assess the prediction capability of the conditioning factors in landslide models. Multicollinearity of these factors was verified using the variance inflation factor, tolerance, and Pearson's correlation coefficient. Finally, the three resulting models were evaluated and compared using the area under the receiver operating characteristic (AUROC) curve, standard error, 95% confidence interval, accuracy, precision, recall, and F-measure. The random forest model showed the AUROC values (0.869), smallest standard error (0.025), narrowest 95% confidence interval (0.819-0.918), highest accuracy value (0.774), highest precision (0.662), and highest F-measure (0.662) for the training dataset. Thus, the random forest model is a promising technique that could be used for landslide susceptibility mapping. (c) 2018 Elsevier B.V. All rights reserved.
机译：本研究的主要目的是探索和比较三种先进的数据挖掘技术，即最佳第一决策树，随机森林和朴素贝叶斯树，以用于中国龙海地区的滑坡敏感性评估。首先，随机划分了具有93个滑坡位置的滑坡清单图，其中70％的面积用于训练滑坡模型，而30％的面积用于验证过程。在地理信息系统环境下，构建了一个包含14个调节因子的空间数据库。随后，采用ReliefF方法评估滑坡模型中条件因子的预测能力。使用方差膨胀因子，公差和Pearson相关系数验证了这些因子的多重共线性。最后，使用接收器工作特性（AUROC）曲线，标准误差，95％置信区间，准确性，精确度，召回率和F量度下的面积对三个结果模型进行了评估和比较。随机森林模型显示AUROC值（0.869），最小标准误差（0.025），最窄的95％置信区间（0.819-0.918），最高准确度值（0.774），最高准确度（0.662）和最高F测度（0.662） ）用于训练数据集。因此，随机森林模型是一种很有前途的技术，可用于滑坡敏感性图。 （c）2018 Elsevier B.V.保留所有权利。

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