A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS

Binh Thai Pham; Dieu Tien Bui; Prakash Indra; Long Hoang Nguyen; Dholakia M. B.

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A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS

机译：基于GIS的连续最小优化支持向量机，投票特征区间和逻辑回归比较研究

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Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of- the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFImodel, and the LRmodel, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.

机译：为了比较三种不同机器学习方法的预测能力，即基于连续最小优化的支持向量机（SMOSVM），投票特征间隔（3），使用GIS对喜马拉雅（印度）的北阿坎德邦地区进行了滑坡敏感性评估。 VFI）和逻辑回归（LR）来进行滑坡发生的空间预测。在这三种方法中，SMOSVM和VFI是用于二元分类问题的最新方法，但尚未应用于滑坡预测，而LR被称为是一种流行的滑坡敏感性评估方法。在研究中，总共有430个历史滑坡多边形和11个滑坡影响因素，例如坡度，坡度，高程，曲率，岩性，土壤，土地覆盖，到公路的距离，到河的距离，到线质的距离以及降雨。选择进行滑坡分析。为了进行验证和比较，已使用基于统计指标的方法和接收器工作特性曲线。分析结果表明，所有这些模型都具有良好的滑坡空间预测性能，但SMOSVM模型具有最高的预测能力，其次是VFI模型和LR模型。因此，SMOSVM是一个更好的滑坡预测模型，可用于易发生滑坡地区的滑坡敏感性图。

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来源
《Environmental earth sciences》 |2017年第11期|371.1-371.15|共15页
作者
Binh Thai Pham; Dieu Tien Bui; Prakash Indra; Long Hoang Nguyen; Dholakia M. B.;
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作者单位

Gujarat Technol Univ, Dept Civil Engn, Nr Visat Three Rd,Visat Gandhinagar Highway, Ahmadabad 382424, Gujarat, India|Univ Transport Technol, Dept Geotech Engn, 54 Trieu Khuc, Hanoi, Vietnam;

Telemark Univ Coll, Dept Business Adm & Comp Sci, Geog Informat Syst Grp, Hallvard Eikas Plass 1, N-3800 Bo, Telemark, Norway;

Govt Gujarat, BISAG, Dept Sci & Technol, Gandhinagar, India;

Univ Transport Technol, 54 Trieu Khuc, Hanoi, Vietnam;

Gujarat Technol Univ, LDCE, Dept Civil Engn, Ahmadabad 380015, Gujarat, India;

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正文语种 eng
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关键词
Landslides; GIS; Sequential minimal optimization (SMO); Support vector machines (SVM); Vote feature intervals (VFI); India;

机译：滑坡;GIS;顺序最小优化（SMO）;支持向量机（SVM）;投票特征区间（VFI）;印度;

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A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS

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