Rainfall Threshold Analysis and Bayesian Probability Method for Landslide Initiation Based on Landslides and Rainfall Events in the Past

Minh, Hien Tran; Kun, Yin

doi:10.4236/ojg.2018.87040

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…Bayesian results of rainfall conditions for the landslide early warning system Previous studies used a one-dimensional Bayesian model to examine cumulative event rainfall, rainfall duration, rainfall intensity, 14-day antecedent rainfall, and 30-day antecedent rainfall based Graph showing the results of the two-dimensional Bayesian model for different 3-day antecedent rainfall and cumulative event rainfall on the daily rainfall (Berti et al 2012;Robins 2016;Gonzalez and Caetano 2017;Dikshit et al 2018;Do and Yin 2018). In this study, rainfall intensity, the hourly maximum rainfall, and the 3-day antecedent rainfall, which are highly correlated with landslide occurrence, were added to the two-dimensional Bayesian model, while the 30-day antecedent rainfall, which was not highly correlated with landslide occurrence, was excluded.…”

Section: Discussionmentioning

confidence: 99%

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

2021

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The purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

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Section: Discussionmentioning

confidence: 99%

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

2021

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“…To overcome the use of empirical models, probabilistic model using Bayes' theorem was developed by [14] is an efficient choice and the same was applied to the Emilia-Romagna region in Italy. Thereafter, similar work was carried out in several places (e.g., Ha Giang region, Vietnam [15], Sierra Norte De Puebla, Mexico [16], Kalimpong, India [17,18], Chibo, India [19]. The use of probabilistic techniques has also been carried out for severe precipitation conditions [20,21].…”

Section: Introductionmentioning

confidence: 99%

Determination of the Probabilities of Landslide Events—A Case Study of Bhutan

Sarkar

Dorji

2019

Hydrology

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Landslides have been and are prominent and devastating natural disasters in Bhutan due to its orography and intense monsoonal rainfall. The damage caused by landslides is huge, causing significant loss of lives, damage to infrastructure and loss of agricultural land. Several methods have been developed to understand the relationship between rainfall and landslide incidences. The most common method to understand the relationship is by defining thresholds using empirical methods which are expressed in either intensity-duration or event rainfall-duration terms. However, such thresholds determine the results in a binary form which may not be useful for landslide cases. Apart from defining thresholds, it is significant to validate the results. The article attempts to address both these issues by adopting a probabilistic approach and validating the results. The region of interest is the Chukha region located along the Phuentsholing-Thimphu Highway, which is a significant trade route between neighbouring countries and the national capital Thimphu. In the present study, probabilities are determined by Bayes’ theorem considering rainfall and landslide data from 2004 to 2014. Singular (rainfall intensity, rainfall duration and event rainfall) along with a combination (rainfall intensity and rainfall duration) of precipitation parameters were considered to determine the probabilities for landslide events. A sensitivity analysis was performed to verify the determined probabilities. The results depict that a combination of rainfall parameters is a better indicator to forecast landslides as compared to single rainfall parameter. Finally, the probabilities are validated using landslide records for 2015 using a threat score. The validation signifies that the probabilities can be used as the first line of action for an operational landslide warning system.

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“…Bayesian Risk Measurement in banking institutions and the construction industry has been carried out by (Eschmann et al, 2019;Erango and Goshu, 2019;Zhou et al 2018;Do and Yin, 2018;Fong et al, 2017;Kim et al 2012;Kumar, 2010). In addition, Zhou et al (2018) did a risk assessment and showed that the Bayesian network method performs risk assessment effectively and works flexibly with offshore wind power construction.…”

Section: Introductionmentioning

confidence: 99%

Engine Room Module Installation System Risk Analysis Based on Bayesian Network

Baroroh,

Ma’ruf,

Basuki

et al. 2024

IJTech

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Implementation of system installation risk on engine room module aims to anticipate ship production delays. However, in its implementation, there is the problem of ship delivery delays in various shipbuilding companies. This research will analyze the risk analysis of the installation of the engine room module system by identifying risk factors and parameters that affect performance as a hazard identification that has the potential to cause delivery delays. The object of the research is the Indonesian Navy's Auxiliary Hospital Ship, which has 6 zones with a pilot project developed in zone 2. This includes an engine room that contains important constructions in the form of a main motor foundation, auxiliary motor, and other machinery, where the system was integrated. Moreover, there are some works consisting of construction, outfitting, and commissioning which are very complicated and require high accuracy. The aim of the implementation of risk analysis of the installation system in the engine room's module is to assess potential risks, the effect of risk on project delays, and project cost overruns. The research method uses a Bayesian network because it is able to assess the most potential risks and predict the possibility of delays at network nodes. The primary risk is associated with electrical activities, specifically electrical outfitting on wiring, clamping, and compound sub-components with a probability of (0.0002560) in the Machinery Outfitting and Electrical Department. This risk stems from inappropriate steps during drawing revisions, which have the potential to cause delays in equipment installation, cable material procurement, and the generation of cable cutting data. Therefore, early coordination with production planning control, design, and the supply chain is crucial.

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Rainfall Threshold Analysis and Bayesian Probability Method for Landslide Initiation Based on Landslides and Rainfall Events in the Past

Cited by 12 publications

References 30 publications

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

Determination of the Probabilities of Landslide Events—A Case Study of Bhutan

Engine Room Module Installation System Risk Analysis Based on Bayesian Network

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