scholarly journals Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method

2020 ◽  
Vol 12 (16) ◽  
pp. 2663 ◽  
Author(s):  
Sunmin Lee ◽  
Jungyoon Jang ◽  
Yunjee Kim ◽  
Namwook Cho ◽  
Moung-Jin Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Probabilistic Method ◽  
Landslide Prediction ◽  
Deterministic Analysis ◽  
Susceptibility Analysis ◽  
Prediction Rate

Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.

Tree-based models for variable annuity valuation: parameter tuning and empirical analysis

2021 ◽  
pp. 1-24
Author(s):  
Zhiyu Quan ◽  
Guojun Gan ◽  
Emiliano Valdez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Prediction Accuracy ◽  
Recursive Partitioning ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Financial Risks ◽  
Risk Quantification ◽  
Boosting Method ◽  
Computationally Intensive

Abstract Variable annuities have become popular retirement and investment vehicles due to their attractive guarantee features. Nonetheless, managing the financial risks associated with the guarantees poses great challenges for insurers. One challenge is risk quantification, which involves frequent valuation of the guarantees. Insurers rely on the use of Monte Carlo simulation for valuation as the guarantees are too complicated to be valued by closed-form formulas. However, Monte Carlo simulation is computationally intensive. In this paper, we empirically explore the use of tree-based models for constructing metamodels for the valuation of the guarantees. In particular, we consider traditional regression trees, tree ensembles, and trees based on unbiased recursive partitioning. We compare the performance of tree-based models to that of existing models such as ordinary kriging and generalised beta of the second kind (GB2) regression. Our results show that tree-based models are efficient in producing accurate predictions and the gradient boosting method is considered the most superior in terms of prediction accuracy.

Uncertainty Analysis and Monitoring of Earth Dam

2014 ◽  
Vol 580-583 ◽  
pp. 954-957
Author(s):  
Ling Qiang Yang ◽  
Rui Gao ◽  
Yan Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probabilistic Method ◽  
Finite Difference Methods ◽  
Earth Dam ◽  
Earth Dams ◽  
Program Code ◽  
Physical Behavior ◽  
Difference Methods

Monte Carlo simulation provides a probabilistic method to evaluate the physical behavior of earth dam. Therefore, the behavior could be got in a more realistic manner. Based on the theory, an innovative software program code is developed by combining the Monte Carlo and finite difference methods to predict the performance of earth dams after impounding. In order to assess the efficiency of the method, the case study of earth dam, located at Southeast of China, has been studied in detail. The performance of this dam is predicted and compared with the field monitoring by using the monitoring data. The results shows the robustness of the proposed method.

scholarly journals AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN

2019 ◽  
Vol IV-2/W5 ◽  
pp. 503-511 ◽  
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
M. B. A. Gibril ◽  
U. S. Lay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Total Curvature ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Inventory Data ◽  
Prediction Rate

<p><strong>Abstract.</strong> Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, RF could be the best algorithm for producing landslide susceptibility map, and such results could be adopted for the decision-making process to support land use planner for improving landslide risk assessment in similar environmental settings.</p>

scholarly journals Simulation of Debris-Flow Runout Near a Construction Site in Korea

2020 ◽  
Vol 10 (17) ◽  
pp. 6079 ◽  
Author(s):  
Byung-Gon Chae ◽  
Ying-Hsin Wu ◽  
Ko-Fei Liu ◽  
Junghae Choi ◽  
Hyuck-Jin Park
Keyword(s):  
Debris Flow ◽  
Landslide Susceptibility ◽  
Debris Flows ◽  
Source Area ◽  
Flow Path ◽  
Construction Site ◽  
Landslide Prediction ◽  
Runout Distance ◽  
Susceptibility Analysis

This study analyzed landslide susceptibility and numerically simulated the runout distance of debris flows near a construction site in Korea. Landslide susceptibility was based on a landslide prediction map of the study area. In the prediction map, 3.5% of the area had a 70–90% landslide probability, while 0.79% had over 90% probability. Based on the landslide susceptibility analysis, debris flows in four watersheds were simulated to assess possible damage to the construction site. According to the simulations, debris flow in Watershed C approaches to within 9.6 m of the site. Therefore, the construction site could be impacted by debris flow in Watershed C. Although the simulated flows in Watersheds A and D do not directly influence the construction site, they could damage the nearby road and other facilities. The simulations also show that debris runout distance is strongly influenced by the volume of debris in the on-slope source area and by the slope angles along the debris-flow path.

scholarly journals Acrylamide in Baby Foods: A Probabilistic Exposure Assessment

Foods ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2900
Author(s):  
Francesco Esposito ◽  
Agata Nolasco ◽  
Francesco Caracciolo ◽  
Salvatore Velotto ◽  
Paolo Montuori ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
High Temperature ◽  
Probabilistic Method ◽  
Primary Source ◽  
Dietary Exposure ◽  
Carcinogenic Effects ◽  
Baked Products ◽  
Potential Carcinogen

Acrylamide (also known as 2-propenamide) (AA) is a toxicant that develops in food during high-temperature cooking, and its occurrence is common in biscuits and baked snacks. AA is known for its in vivo neurotoxic and carcinogenic effects, and it is considered a potential carcinogen for humans. Infants may be exposed to AA as early as during weaning through baked food such as biscuits. This study set out to ascertain the concentration of AA in food products intended for infants to assess the dietary exposure to this food contaminant. AA levels were determined through GC/MS and bromination, and dietary exposure was evaluated by a probabilistic method based on Monte Carlo simulation. The results showed that the probability of a carcinogenic exposure is 94%, 92%, and 87%, respectively, for 6-, 12-, and 18-months infants, suggesting the need to delay the introduction of baked products in the diet of weaned infants. It should be noted, however, that these conclusions were drawn considering the biscuits as the primary source of exposure.

scholarly journals Applying probabilistic method base on Monte Carlo simulation to analyze slope stability on route Nha Trang - Da Lat (within Khanh Vinh district, Khanh Hoa province)

2014 ◽  
Vol 17 (3) ◽  
pp. 76-85
Author(s):  
Danh Thanh Nguyen ◽  
Ngo Van Dau ◽  
Dung Quoc Ta
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Slope Stability ◽  
Soil Properties ◽  
Reliability Index ◽  
Slope Failure ◽  
Probabilistic Method ◽  
Optimal Designs ◽  
Engineering Practice ◽  
Method Base

In this paper, a Monte Carlo simulation used to analyze probabilistic slope stability. The results including: probabilistic slope failure and reliability index with respect to factor of safety under the effects of uncertainties in the parameters of soil properties. Base on this informations, geotechnical engineers how to get optimal designs to prevent slope failure. In addition, the purpose of this paper is to show that standard deviation of soil properties can be applied in simple ways, without more data, time, or effort than are commonly available in geotechnical engineering practice. Applying Monte Carlo simulation to evaluate probabilistic slope stability on route Nha Trang - Da Lat.

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