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Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

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Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of model hyperparameters is of great importance to the accuracy and precision of one landslide hazard assessment model. In this study, Bayesian Optimization (BO) method was used to tune the hyperparameters of Support Vector Machine (SVM) model to obtain a high accuracy landslide hazard zoning map. 1711 historical landslide disaster points were obtained as landslide inventory in a case of Nanping City landslide hazard assessment. A total of 12 factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected as landslide conditional factors. The multicollinearity diagnosis was performed on factors using the Spearman correlation coe cient. 1711 landslides and 1711 non-landslides were collected as the dataset and divided into the same number of training dataset and testing dataset. The confusion matrix and receiver operating characteristic (ROC) curve were used to verify the models. The results of confusion matrix accuracy and the area under ROC curve (AUC) showed that BO-SVM (89.53%, 97%) performed better than only SVM (84.91%, 0.93), which indicated the superiority of the proposed method during this study.

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Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of model hyperparameters is of great importance to the accuracy and precision of one landslide hazard assessment model. In this study, Bayesian Optimization (BO) method was used to tune the hyperparameters of Support Vector Machine (SVM) model to obtain a high accuracy landslide hazard zoning map. 1711 historical landslide disaster points were obtained as landslide inventory in a case of Nanping City landslide hazard assessment. A total of 12 factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected as landslide conditional factors. The multicollinearity diagnosis was performed on factors using the Spearman correlation coefficient. 1711 landslides and 1711 non-landslides were collected as the dataset and divided into the same number of training dataset and testing dataset. The confusion matrix and receiver operating characteristic (ROC) curve were used to verify the models. The results of confusion matrix accuracy and the area under ROC curve (AUC) showed that BO-SVM (89.53%, 97%) performed better than only SVM (84.91%, 0.93), which indicated the superiority of the proposed method during this study.

The pandemic has challenged countries to develop stringent measures to reduce infections and keep the population healthy. However, the greatest challenge is understanding the process of adopting self-care measures by individuals in different countries. In this research, we sought to understand the behavior of individuals who take self-protective action. We selected the risk homeostasis approach to identify relevant variables associated with the risk of contagion and the Protective Action Decision Model to understand protective decision-making in the pandemic. Subsequently, we conducted an exploratory survey to identify whether the same factors, as indicated in the literature, impact Chile’s adoption of prevention measures. The variables gender, age, and trust in authority behave similarly to those found in the literature. However, socioeconomic level, education, and media do not impact the protection behaviors adopted to avoid contagion. Furthermore, the application of the Protective Action Decision Model is adequate to understand the protective measures in the case of a pandemic. Finally, women have a higher risk perception and adopt more protective measures, and in contrast, young people between 18 and 30 years of age are the least concerned about COVID-19 infection.