Examinando por Autor "Robledo, Luis F."

Mostrando 1 - 4 de 4
  • No hay miniatura disponible
    Ítem
    Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China
    (Springer Science and Business Media B.V., 2021-10) Xie, Wei; Nie, Wen; Nie W.; Saffari, Pooya; Robledo, Luis F.; Descote, Pierre-Yves; Jian, Wenbin
    Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model.
  • No hay miniatura disponible
    Ítem
    A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China
    (MDPI, 2021-02-20) Xie, Wei; Li, Xiaoshuang; Jian, Wenbin; Yang, Yang; Liu, Hongwei; Robledo, Luis F.; Nie, Wen
    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.
  • Miniatura
    Ítem
    Prediction of Matrix Suction of Unsaturated Granite Residual Soil Slope Based on Electrical Conductivity
    (Frontiers Media S.A., 2022-03) Chen, Ruimin; Lin, Yunzhao; Liu, Qingling; Dou, Hongqiang; Robledo, Luis F.; Jian, Wenbin
    To study the relationship between matrix suction and conductivity in unsaturated granite residual soil and realize the matrix suction prediction of soil slope based on conductivity, laboratory and field tests are carried out on undisturbed soil at different depths of the Yandou village landslide in Sanming City, Fujian Province, China. Through physical and chemical property analysis, soil-water characteristic curves and electric parameter matrix suction prediction models for unsaturated granite residual soil at different depths of the target area are obtained. Based on the proposed model, the matrix suction distribution of on-site soil slope is predicted and the dynamic response law under the influence of artificial rainfall is studied. The results show that: (1) The transverse conductivity, average structure factor, average shape factor, and anisotropy coefficient of unsaturated soil are related to the soil saturation degree. By considering the above parameters, the comprehensive structure parameter Re is introduced and its functional relationship with matrix suction is established. (2) Under artificial simulated rainfall, the saturation, hysteresis of the conductivity parameters, and matrix suction response of the slope occurs, which is controlled by soil depth, permeability and rainfall intensity. The matrix suction is distributed in layers on the profile and its recovery rate is slower than saturation. The suction contour map shows a parabola shape with the opening downward. (3) The relationship between the conductivity parameters of the residual soil slope and matrix suction is further revealed and a new method to indirectly measure matrix suction is proposed. Its feasibility is verified based on field tests, which is of great significance to landslide monitoring and early warning. Copyright © 2022 Chen, Lin, Liu, Dou, Robledo and Jian.
  • Miniatura
    Ítem
    Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case
    (MDPI, 2023-07) Maragaño-Carmona, Gonzalo; Fustos Toribio, Ivo J.; Descote, Pierre-Yves; Robledo, Luis F.; Villalobos, Diego; Gatica, Gustavo
    The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact.