scholarly journals Exploring the potential relationship between the occurrence of landslides and debris flows: A new approach

2020 ◽  
Author(s):  
Zhu Liang ◽  
Changming Wang ◽  
Kaleem Ullah Jan Khan
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Debris Flows ◽  
New Approach ◽  
High Class ◽  
Potential Relationship ◽  
Inventory Map ◽  
Auc Value ◽  
Landslides And Debris Flows ◽  
Very High

Abstract. The aim of the present study is to explore the potential relationship between landslides and debris flows by establishing susceptibility zoning maps separately with the use of random forest. Longzi township, Longzi County, located in Southeastern Tibet, where historical landslide and debris flow are commonly occurred, was selected as the study area. The work has been carried out with the following steps: (1) A complete landslide and debris flow inventory map was prepared; (2) Slope units and 11 controlling factors were prepared for the susceptibility modelling of landslide while watershed units and 12 factors for debris flow; (3) Establishing susceptibility zoning maps for landslide and debris flow, respectively, with the use of random forest; (4) The performance of two models are verified using ROC curve, the values of AUC and contingency tables; (5) Putting the high or very-high-class watershed units in the debris flow susceptibility zone map as the base map to observe its coverage by slope units of different classes; (6) The landslide zoning map was put at the bottom floor and analyzed the distribution of high or very-high-class slope units in watershed units; (7) transforming the slope units into points and distributed them on the watershed units. Two models based on random forest have demonstrated great predictive capabilities, of which accuracy was close to 90% and the AUC value was close to 1. The loose sources carried out by the debris flows are not necessarily brought by the landslides although most landslides can be converted into debris flows. The area prone to debris flow does not promote the occurrence of landslides. A susceptibility zoning map composed of two or more natural disasters is comprehensive and significant in this regard.

scholarly journals GIS-based modelling of landslide and debris flow hazard in mountainous terrain of Agra Khola watershed, central Nepal

2006 ◽  
Vol 34 ◽  
pp. 117-128
Author(s):  
P. B. Thapa ◽  
T. Esaki ◽  
B. N. Upreti
Keyword(s):  
Debris Flow ◽  
Prediction Model ◽  
Debris Flows ◽  
Flow Distribution ◽  
Spatial Prediction ◽  
Distribution Map ◽  
Central Nepal ◽  
Landslides And Debris Flows ◽  
Debris Flow Hazard ◽  
Hazard Levels

A comprehensive GIS-based analytical approach was followed to derive a spatial database of landslides and debris flows in the Agra Khola watershed of central Nepal which suffered from the hydrological disaster of 1993. For this purpose, the landslides and debris flows occurring in that area between 1993 and 2006 were delineated. From the database, the influence of geological and geomorphic variables was quantified and a spatial prediction model for landslide and debris flow hazard was worked out. In this process, quantitative statistical analysis (bivariate, multivariate) as applied to predict elements or observations between stable and unstable zones. The predicted results were classified into various hazard levels m a hazard map and were validated by comparing it with the landslide and debris flow distribution map of the Agra Khola watershed. Also the GIS-based hazard prediction model has objectivity in the procedure and reproducibility of the results in the mountainous terrains.

scholarly journals Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

2021 ◽  
Vol 21 (9) ◽  
pp. 2773-2789
Author(s):  
Jacob Hirschberg ◽  
Alexandre Badoux ◽  
Brian W. McArdell ◽  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Multiple Scales ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Early Warning Systems ◽  
Antecedent Rainfall ◽  
Data Set

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

scholarly journals Development of Nomogram for Debris Flow Forecasting Based on Critical Accumulated Rainfall in South Korea

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2181
Author(s):  
Nam ◽  
Lee ◽  
Kim
Keyword(s):  
South Korea ◽  
Debris Flow ◽  
Urban Areas ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Accumulated Rainfall ◽  
Landslides And Debris Flows ◽  
Heavy Rains

Climate change causes extreme weather events worldwide such as increasing temperatures and changing rainfall patterns. With South Korea facing growing damage from the increased frequency of localized heavy rains. In particular, its steep slope lands, including mountainous areas, are vulnerable to damage from landslides and debris flows. In addition, localized short-term heavy rains that occur in urban areas with extremely high intensity tend to lead a sharp increase in damage from soil-related disasters and cause huge losses of life and property. Currently, South Korea forecasts landslides and debris flows using the standards for forecasting landslides and heavy rains. However, as the forecasting is conducted separately for rainfall intensity and accumulated rainfall, this lacks a technique that reflects both amount and intensity of rainfall in an episode of localized heavy rainfall. In this study, aims to develop such a technique by collecting past cases of debris flow occurrences and rainfall events that accompanied debris flows to calculate the rainfall triggering index (RTI) reflecting accumulated rainfall and rainfall intensity. In addition, the RTI is converted into the critical accumulated rainfall (Rc) to use rainfall information and provide real-time forecasting. The study classifies the standards for flow debris forecasting into three levels: ALERT (10–50%), WARNING (50–70%), and EMERGENCY (70% or higher), to provide a nomogram for 6 h, 12 h, and 24 h. As a result of applying this classification into the actual cases of Seoul, Chuncheon, and Cheongju, it is found that about 2–4 h of response time is secured from the point of the Emergency level to the occurrence of debris flows.

scholarly journals Application of simulation technique on debris flow hazard zone delineation: a case study in the Daniao tribe, Eastern Taiwan

2011 ◽  
Vol 11 (11) ◽  
pp. 3053-3062 ◽  
Author(s):  
M. P. Tsai ◽  
Y. C. Hsu ◽  
H. C. Li ◽  
H. M. Shu ◽  
K. F. Liu
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Large Scale ◽  
Sensitivity Factor ◽  
Original Design ◽  
Initial Volume ◽  
Front Position ◽  
Landslides And Debris Flows ◽  
Numerical Program

Abstract. Typhoon Morakot struck Taiwan in August 2009 and induced considerable disasters, including large-scale landslides and debris flows. One of these debris flows was experienced by the Daniao tribe in Taitung, Eastern Taiwan. The volume was in excess of 500 000 m3, which was substantially larger than the original design mitigation capacity. This study considered large-scale debris flow simulations in various volumes at the same area by using the DEBRIS-2D numerical program. The program uses the generalized Julien and Lan (1991) rheological model to simulate debris flows. In this paper, the sensitivity factor considered on the debris flow spreading is the amount of the debris flow initial volume. These simulated results in various amounts of debris flow initial volume demonstrated that maximal depths of debris flows were almost deposited in the same area, and also revealed that a 20% variation in estimating the amount of total volume at this particular site results in a 2.75% variation on the final front position. Because of the limited watershed terrain, the hazard zones of debris flows were not expanded. Therefore, the amount of the debris flow initial volume was not sensitive.

Loss Curve Analysis of a Debris Flow: A Case Study on the Daniao Tribe

2013 ◽  
Vol 284-287 ◽  
pp. 1499-1510 ◽  
Author(s):  
Hsin Chi Li ◽  
Ko Fei Liu ◽  
Yu Charn Hsu
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Large Scale ◽  
Financial Burden ◽  
Economic Loss ◽  
Loss Assessment ◽  
Governmental Agencies ◽  
Precautionary Measures ◽  
Exposure Evaluation ◽  
Landslides And Debris Flows

The post-disaster recovery from a debris flow imposes a heavy financial burden on a government; thus, it is necessary for governmental agencies use their limited resources effectively. In this study, we focus on the loss curve assessment, the framework of which includes hazard simulation, exposure evaluation and loss assessment. A numerical program named DEBRIS-2D is used to simulate debris flow hazards of different scale. Then, the latest land usage data and the economic loss model are factored in for exposure and loss assessment. In August 2009, Typhoon Morakot hit Taiwan and horrendous disasters resulted, including large-scale landslides and debris flows. The Daniao tribe in Taitung in eastern Taiwan was hit by one of these debris flows, with a debris-flow volume in excess of 500,000 m3, which was beyond the scope of any recovery plan. We simulate the hazards of the debris flow in different volumes and build the loss curve in relation. Notably, the practice of assessing damage caused by such disasters can help in assessing possible losses before they occur, thus allowing governments to take the necessary precautionary measures.

scholarly journals Exploring the potential relationship between the occurrence of debris flow and landslide

2020 ◽  
Author(s):  
Zhu Liang ◽  
Changming Wang ◽  
Donghe Ma ◽  
Kaleem Ullah Jan Khan
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Susceptibility Map ◽  
Relative Operating Characteristic ◽  
Conditioning Factors ◽  
Statistical Measures ◽  
Potential Relationship ◽  
Major Factors

Abstract. he aim of the present study is to explore the potential relationship between debris flow and soil slide by establishing susceptibility zoning maps (SZM) separately with the use of random forest. Longzi County, located in Southeastern Tibet, where historical landslides occurred commonly, was selected as the study area. The work has been carried out with the following steps: (1) An inventory map consisting of 448 landslides (399 soil slides and 49 debris flows) was determined; (2) Slope units and 11 conditioning factors were prepared for the susceptibility modelling of landslide while watershed units and 12 factors for debris flow; (3) SZM were constructed for landslide and debris flow, respectively, with the use of random forest; (4) The performance of two models were evaluated by 5-fold cross-validation using relative operating characteristic curve (ROC), area under the curve (AUC) and statistical measures; (5) The potential relationship between soil slide and debris flow was explored by the superimposition of two zoning maps; (6) Gini index was applied to determined the major factors and analyze the difference between debris flow and soil slide; (7) A combined susceptibility map with two kinds of disaster was obtained. Two models had demonstrated great predictive capabilities, of which accuracy and AUC was 87.33 %, 0.902 and 85.17 %, 0.892, respectively. The loose sources need by the debris flow were not necessarily brought by the landslides although most landslides can be converted into debris flow. The area prone to debris flow did not promote the occurrence of landslide. A susceptibility zoning map composed of two or more natural disasters is comprehensive and significant in this regard, which provides valuable reference for researches of disaster-chain and engineering applications.

scholarly journals Evaluation of predictive models for post-fire debris flow occurrence in the western United States

2018 ◽  
Vol 18 (9) ◽  
pp. 2331-2343 ◽  
Author(s):  
Efthymios I. Nikolopoulos ◽  
Elisa Destro ◽  
Md Abul Ehsan Bhuiyan ◽  
Marco Borga ◽  
Emmanouil N. Anagnostou
Keyword(s):  
United States ◽  
Random Forest ◽  
Debris Flow ◽  
Predictive Models ◽  
Debris Flows ◽  
The United States ◽  
Western United States ◽  
Economic Losses ◽  
United States Geological Survey ◽  
Fire Debris

Abstract. Rainfall-induced debris flows in recently burned mountainous areas cause significant economic losses and human casualties. Currently, prediction of post-fire debris flows is widely based on the use of power-law thresholds and logistic regression models. While these procedures have served with certain success in existing operational warning systems, in this study we investigate the potential to improve the efficiency of current predictive models with machine-learning approaches. Specifically, the performance of a predictive model based on the random forest algorithm is compared with current techniques for the prediction of post-fire debris flow occurrence in the western United States. The analysis is based on a database of post-fire debris flows recently published by the United States Geological Survey. Results show that predictive models based on random forest exhibit systematic and considerably improved performance with respect to the other models examined. In addition, the random-forest-based models demonstrated improvement in performance with increasing training sample size, indicating a clear advantage regarding their ability to successfully assimilate new information. Complexity, in terms of variables required for developing the predictive models, is deemed important but the choice of model used is shown to have a greater impact on the overall performance.

Massflow—A software for dynamic modeling and risk evaluation of earth-surfaced flow

2021 ◽  
Author(s):  
Chaojun Ouyang
Keyword(s):  
Finite Difference ◽  
Finite Difference Method ◽  
Debris Flow ◽  
Dynamic Modeling ◽  
Difference Method ◽  
Debris Flows ◽  
Risk Evaluation ◽  
Flow Prediction ◽  
Landslides And Debris Flows ◽  
Rainfall Flood

<p>Massflow is based on the depth-integrated continuum and solved by second-order MacCormack-TVD finite difference method. Shared code and friendly GUI are provided for researchers and engineers. It adopted CPU and GPU accellerated algorithm to improve the efficiency. Now around 1000 people adopted Massflow to do their own research. Based the framework, we have done several insightful simulations of real landslides and debris flows. Meanwhile, we are developing a solution for catchment-based rainfall- flood-debris flow prediction. We will introduce the basic of the software, the mechanism and related model to modeling the real hazards, and the framework and finished work of forecasting of catchment flood or debris flow. </p>

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