Abstract

This paper describes the development and validation of landslides susceptibility models for mountainous regions using advanced data mining techniques. The investigation was carried out to ascertain the effectiveness of Naïve Bayes Multinomial (NBM) and Random Trees (RT) in landslide susceptibility mapping. The NBM is an advancement of the frequently used Naïve Bayes classifiers, while the RT was built to overcome the limitations of the traditional forest classifiers. A geospatial database for this investigation comprises 148 landslide locations influenced by ten (10) landslide conditioning factors. The factors (Slope Angle, Slopes Elevation, Slope Aspect, Plan curvature, Profile Curvature, Lithology, Soil type, Stream power index (SPI), Sediment transport index (STI), and Rainfall precipitation) were drawn using a Multi Collinearity Decision Making (MCDM) technique. A Frequency Ratio (FR) analysis was used to obtain the relative significance of the factors in the slides. Predictive models were also developed by quantifying these models using data mining techniques. A section of the entire geospatial data (70%) was used as training datasets, while the remaining part of the data (30%) was used to validate the trained datasets. SVM, RT, and NBM algorithms were used to produce predicted datasets from the training datasets. These predicted datasets were used to develop the Landslides Susceptibility Models. A comparative assessment between the two classifiers against the famous traditional learning algorithm, the Support vector machines (SVM), was conducted. Model performance evaluators such as the AUROC, RSME, F-measure, MAE, and ACC were employed to check the predictive capabilities and accuracies of the models. The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. Further analysis and comparison of the models reveal that the new data mining techniques are reliable for landslide susceptibility. Simultaneously, the traditional algorithm is also useful and remains relevant, especially with similar site conditions. This study has provided insights on better planning and development and provision of mitigation strategies and further analysis on landslides in the study area, particularly in cases of limited data availability.

摘要

本文描述了使用高级数据挖掘技术开发和验证山区滑坡敏感性模型。进行调查是为了确定朴素贝叶斯多项式 (NBM) 和随机树 (RT) 在滑坡敏感性绘图中的有效性。NBM 是对常用朴素贝叶斯分类器的改进，而 RT 旨在克服传统森林分类器的局限性。本次调查的地理空间数据库包含受十 (10) 个滑坡条件因素影响的 148 个滑坡位置。因素（坡角、坡度、坡向、平面曲率、剖面曲率、岩性、土壤类型、河流功率指数 (SPI)、沉积物运移指数 (STI)、和降雨降水）是使用多重共线性决策 (MCDM) 技术绘制的。使用频率比 (FR) 分析来获得载玻片中因素的相对显着性。还通过使用数据挖掘技术量化这些模型来开发预测模型。整个地理空间数据的一部分 (70%) 用作训练数据集，而其余部分数据 (30%) 用于验证训练后的数据集。SVM、RT 和 NBM 算法用于从训练数据集生成预测数据集。这些预测数据集用于开发滑坡敏感性模型。对两个分类器与著名的传统学习算法支持向量机 (SVM) 进行了比较评估。模型性能评估器，例如 AUROC、RSME、F-measure、MAE 和 ACC 被用来检查模型的预测能力和准确性。指标表明 SVM 模型在训练和验证数据集中的表现都优于其他两种算法。对模型的进一步分析和比较表明，新的数据挖掘技术对于滑坡敏感性是可靠的。同时，传统算法也很有用并且保持相关性，尤其是在相似的场地条件下。这项研究为更好地规划和发展以及提供缓解策略和进一步分析研究区域的滑坡提供了见解，特别是在数据有限的情况下。指标表明 SVM 模型在训练和验证数据集中的表现都优于其他两种算法。对模型的进一步分析和比较表明，新的数据挖掘技术对于滑坡敏感性是可靠的。同时，传统算法也很有用并且保持相关性，尤其是在相似的场地条件下。这项研究为更好地规划和发展以及提供缓解策略和进一步分析研究区域的滑坡提供了见解，特别是在数据有限的情况下。指标表明 SVM 模型在训练和验证数据集中的表现都优于其他两种算法。对模型的进一步分析和比较表明，新的数据挖掘技术对于滑坡敏感性是可靠的。同时，传统算法也很有用并且保持相关性，尤其是在相似的场地条件下。这项研究为更好地规划和发展以及提供缓解策略和进一步分析研究区域的滑坡提供了见解，特别是在数据有限的情况下。传统算法也很有用，并且仍然具有相关性，尤其是在类似的场地条件下。这项研究为更好地规划和发展以及提供缓解策略和进一步分析研究区域的滑坡提供了见解，特别是在数据有限的情况下。传统算法也很有用，并且仍然具有相关性，尤其是在类似的场地条件下。这项研究为更好地规划和发展以及提供缓解策略和进一步分析研究区域的滑坡提供了见解，特别是在数据有限的情况下。