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Landslide susceptibility mapping using state-of-the-art machine learning ensembles
Geocarto International ( IF 3.8 ) Pub Date : 2021-05-03 , DOI: 10.1080/10106049.2021.1914746
Binh Thai Pham 1 , Vinh Duy Vu 2 , Romulus Costache 3 , Tran Van Phong 4 , Trinh Quoc Ngo 1 , Trung-Hieu Tran 1 , Huu Duy Nguyen 5 , Mahdis Amiri 6 , Mai Thanh Tan 4 , Phan Trong Trinh 4 , Hiep Van Le 1 , Indra Prakash 7
Affiliation  

Abstract

This study propose a new approach through which the landslide susceptibility in Quang Nam (Vietnam) will be estimated using the best model among the following algorithms: Decision Table (DT), Naïve Bayes (NB), Decision Table - Naïve Bayes (DTNB), Bagging Ensemble, Cascade Generalization Ensemble, Dagging Ensemble, Decorate Ensemble, MultiBoost Ensemble, MultiScheme Ensemble, Real Ada Boost Ensemble, Rotation Forest Ensemble, Random Sub Space Ensemble. In this regard, a map with 1130 landslide, was created and further partitioned into training (70%) and testing (30%) locations. The correlation-based features selections (CFS) method was used to select a number of 15 landslide influencing factors. Landslide locations, included in the training sample, and the landslide predictors were used as input data in order to run the above mentioned models. Kappa index, Accuracy (%) and ROC curve were employed to estimate the model’s performance and to test the outcomes provided by the models. Among the eleven machine learning algorithms, Random Sub Space Decision Table Naïve Bayes (RSSDTNB) was the most performant model with an AUC = 0.839, Accuracy = 76.55% and Kappa Index = 0.531. Therefore, this algorithm was involved in the estimation of landslide susceptibility. The Success Rate (AUC = 0.815) and Prediction Rate (AUC = 0.826) revealed the achievement of high-quality results.



中文翻译:

使用最先进的机器学习集成绘制滑坡敏感性地图

摘要

本研究提出了一种新方法,通过该方法,将使用以下算法中的最佳模型估计广南(越南)的滑坡敏感性:决策表(DT)、朴素贝叶斯(NB)、决策表 - 朴素贝叶斯(DTNB)、 Bagging Ensemble、Cascade Generalization Ensemble、Dagging Ensemble、Decorate Ensemble、MultiBoost Ensemble、MultiScheme Ensemble、Real Ada Boost Ensemble、Rotation Forest Ensemble、Random Sub Space Ensemble。在这方面,创建了一张包含 1130 个滑坡的地图,并进一步划分为训练(70%)和测试(30%)位置。使用基于相关性的特征选择(CFS)方法选择了15个滑坡影响因素。包含在训练样本中的滑坡位置和滑坡预测器被用作输入数据,以运行上述模型。采用 Kappa 指数、准确度 (%) 和 ROC 曲线来估计模型的性能并测试模型提供的结果。在 11 种机器学习算法中,随机子空间决策表朴素贝叶斯 (RSSDTNB) 是性能最高的模型,AUC = 0.839,准确度 = 76.55%,Kappa 指数 = 0.531。因此,该算法参与了滑坡敏感性的估计。成功率 (AUC = 0.815) 和预测率 (AUC = 0.826) 揭示了高质量结果的实现。531. 因此,该算法参与了滑坡敏感性的估计。成功率 (AUC = 0.815) 和预测率 (AUC = 0.826) 揭示了高质量结果的实现。531. 因此,该算法参与了滑坡敏感性的估计。成功率 (AUC = 0.815) 和预测率 (AUC = 0.826) 揭示了高质量结果的实现。

更新日期:2021-05-03
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