Abstract

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.

摘要

山体滑坡是威胁人类生命和财产的危险事件。该研究旨在分析斯洛伐克 Kysuca 河流域的滑坡敏感性 (LS)。出于这个原因，以前的滑坡事件用 16 个滑坡条件因素进行了分析。滑坡清单分为训练（70% 的滑坡位置）和验证数据集（30% 的滑坡位置）。首先应用模糊决策试验和评估实验室（FDEMATEL）-分析网络过程（ANP）的启发式方法，其次是二元频率比（FR）、多元逻辑回归（LR）、随机森林分类器（RFC）、朴素贝叶斯分别是分类器 (NBC) 和极限梯度提升 (XGBoost)。结果显示，52.2%、36.5%、40.7%、50.6%、43.6%和40。FDEMATEL-ANP、FR、LR、RFC、NBC和XGBoost模型分别对应于总流域面积的3%具有非常高到高的LS。分析表明，RFC 是最准确的模型（总体准确率为 98.3%，AUC 为 97.0%）。此外，FDEMATEL-ANP模型的启发式方法（总准确率为93.8%，AUC为92.4%）比二元FR（总准确率为86.9%，AUC为86.1%）、多元LR（总准确率为90.5%）具有更好的预测能力和 AUC 为 91.2%）、机器学习 NBC（总体准确率为 76.3% 和 AUC 为 90.9%）甚至深度学习 XGBoost（总体准确率为 92.3% 和 AUC 为 87.1%）模型。研究表明，FDEMATEL-ANP 胜过 NBC 和 XGBoost 机器学习模型，这表明在直接应用机器学习模型之前应该测试启发式方法。