College of Geography and Resources, Sichuan Normal University, Chengdu, 610101, China.
Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Ministry of Education, Sichuan Normal University, Chengdu, 610101, China.
Environ Sci Pollut Res Int. 2023 Aug;30(38):88612-88626. doi: 10.1007/s11356-023-28730-3. Epub 2023 Jul 13.
Landslides are a common natural disaster, having severe socio-economic effects and posing immense threat to safety, such as loss of life at a global scale. Modeling and predicting the possibility of landslides are important in order to monitor and prevent their negative consequences. In this study, landslides are the primary research object. Further, the frequency ratio (FR) method was applied to the random forest (RF), support vector machine (SVM), and decision tree (DT) regression algorithms for landslide sensitivity assessment. It was also applied to landslide risk assessment mapping in the Longmen Mountain area. Therefore, taking into account the positive and negative sample balance, 7774 historical landslide points and 7774 non-landslide points were selected and divided them into training sets and test sets. The influence factors were selected and analyzed through multicollinearity analysis and the FR method. To improve the performance of the model and the accuracy of the findings, the individual environmental factors are normalized. Subsequently, the LSI (landslide susceptibility index), was obtained by calculating the frequency ratio. Following this, the RF, SVM, and DT were used to construct the model. The trained model calculates the landslide probability of each cell in the study area and generates the resultant susceptibility map. The receiver operating characteristic (ROC) curve and R of this region were calculated to evaluate the model's performance. The results indicate that RF obtained the highest predictive performance (area under the curve (AUC) = 0.82) in landslide risk prediction, followed by SVM (AUC = 0.8) and DT (AUC = 0.69). The results of this study serve as a predictive map for landslide susceptibility areas and provide critical support for the security of lives and property for the human and socio-economic development in the Longmen Mountain region. In addition, the experiment results reveal that the machine learning model based on the FR method can improve the accuracy and performance of methods in studies related to landslide susceptibility. The method is equally applicable to research in other fields.
滑坡是一种常见的自然灾害，对社会经济有严重影响，对安全构成巨大威胁，如全球范围内的生命损失。为了监测和预防滑坡的负面影响，对滑坡的可能性进行建模和预测是很重要的。在本研究中，滑坡是主要的研究对象。进一步将频率比 (FR) 方法应用于随机森林 (RF)、支持向量机 (SVM) 和决策树 (DT) 回归算法，进行滑坡敏感性评估。还将其应用于龙门山地区的滑坡风险评估图绘制。因此，考虑到正负样本平衡，选择了 7774 个历史滑坡点和 7774 个非滑坡点，并将其分为训练集和测试集。通过共线性分析和 FR 方法对影响因素进行选择和分析。为了提高模型的性能和结果的准确性，对单个环境因素进行归一化。然后，通过计算频率比得到 LSI（滑坡敏感性指数）。接下来，使用 RF、SVM 和 DT 构建模型。训练后的模型计算研究区域每个单元的滑坡概率，并生成相应的敏感性图。计算该区域的接收者操作特征 (ROC) 曲线和 R，以评估模型的性能。结果表明，RF 在滑坡风险预测中获得了最高的预测性能（曲线下面积 (AUC) = 0.82），其次是 SVM（AUC = 0.8）和 DT（AUC = 0.69）。本研究的结果为滑坡敏感性区域提供了预测图，为龙门山地区的生命和财产安全以及人类和社会经济发展提供了重要支持。此外，实验结果表明，基于 FR 方法的机器学习模型可以提高与滑坡敏感性研究相关方法的准确性和性能。该方法同样适用于其他领域的研究。
