Abstract The primary aim of this research paper is to develop an easy-to-use tool package called Landslide Susceptibility Mapping Tool Pack (LSM Tool Pack) for producing landslide susceptibility maps based on integrating R with ArcMap Software. The proposed tool contains 5 main modules namely: (1) Data Preparation (DP), (2) Feature (Factor) Selection (FS), (3) Logistic Regression (LR), (4) Random Forest (RF) and (5) Performance Evaluation (PE). The FS module brings a novel approach to determine the best factor subset in the production of landslide susceptibility maps. The feature ranking values of factors were calculated by several feature ranging methods (i.e. chi-square, information gain, rank correlation, and random forest feature importance). The logistic regression method was used at the model prediction stage for each feature ranking and different models were produced for each ranking result. And, in the last step of the FS analysis, tests of statistical significance (i.e. Wilcoxon signed-rank test, F- Test, Kolmogorov Smirnov test, and One-Sample t-test) were used to determine the significance of the difference between models. As a result, the best factor sets determined by the FS module were used as input factors in the LR Module and the RF Module to produce LSMs. Also, users can calculate the performance metric of landslide susceptibility maps by several performance metrics (overall accuracy, Area under the ROC Curve (AUC) value, kappa, F1 score, and more) with additional integrated the PE Module in ArcMap Software. The LSM Tool Pack is applied to the Sinop province of the Black Sea region of Turkey. Considered the FS module, Case 1 was selected as the experimental dataset for this present study. In the selected Case 1, feature ranking method and statistically significant analysis were determined by Chi-square and F-Test, respectively. As a result, Model-12, which is contained 12 landslide causative factors, was determined as the optimum subset. According to the results obtained by the accuracy assessment process, the RF model showed the best prediction performance with an AUC value of 0.8898. On the other hand, the calculated AUC value was 0.8119 for the LR model. The experimental results (using with dataset in actual study area) confirm the ability of the proposed feature selection approach in the landslide susceptibility mapping process.

中文翻译：

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为滑坡敏感性绘图开发综合地理计算工具：LSM 工具包

摘要 本研究论文的主要目的是开发一个易于使用的工具包，称为滑坡敏感性绘图工具包（LSM Tool Pack），用于基于 R 与 ArcMap 软件的集成制作滑坡敏感性地图。所提出的工具包含 5 个主要模块，即：（1）数据准备（DP），（2）特征（因子）选择（FS），（3）逻辑回归（LR），（4）随机森林（RF）和（5 ) 绩效评估 (PE)。FS 模块提供了一种新方法来确定滑坡敏感性图制作中的最佳因子子集。通过多种特征测距方法（即卡方、信息增益、秩相关和随机森林特征重要性）计算因子的特征排序值。在模型预测阶段对每个特征排序使用逻辑回归方法，并为每个排序结果生成不同的模型。并且，在 FS 分析的最后一步，使用统计显着性检验（即 Wilcoxon 符号秩检验、F 检验、Kolmogorov Smirnov 检验和单样本 t 检验）来确定模型之间差异的显着性. 因此，由 FS 模块确定的最佳因子集被用作 LR 模块和 RF 模块中的输入因子以生成 LSM。此外，用户还可以通过多个性能指标（总体精度、ROC 曲线下面积 (AUC) 值、kappa、F1 分数等）计算滑坡敏感性图的性能指标，另外还集成了 ArcMap 软件中的 PE 模块。LSM 工具包适用于土耳其黑海地区的锡诺普省。考虑到 FS 模块，案例 1 被选为本研究的实验数据集。在选定的案例 1 中，特征排序方法和统计显着性分析分别由卡方和 F 检验确定。因此，包含12个滑坡成因的Model-12被确定为最优子集。根据精度评估过程得到的结果，RF模型显示出最好的预测性能，AUC值为0.8898。另一方面，对于 LR 模型，计算出的 AUC 值为 0.8119。实验结果（与实际研究区的数据集一起使用）证实了所提出的特征选择方法在滑坡敏感性绘图过程中的能力。