scholarly journals Empirical assessment of rockfall and debris flow risk along the Karakoram Highway, Pakistan

2021 ◽  
Author(s):  
Sajid Ali ◽  
Rashid Haider ◽  
Wahid Abbas ◽  
Muhammad Basharat ◽  
Klaus Reicherter
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Southwest China ◽  
Tectonic Setting ◽  
Site Specific ◽  
Rockfall Hazard ◽  
North Pakistan ◽  
Karakoram Highway ◽  
Hazard Rating ◽  
Very High

AbstractThe Karakoram Highway links north Pakistan with southwest China. It passes through unique geomorphological, geological and tectonic setting. This study focused 200-km-long section of the highway starting from Besham until Chilas. Landslides are frequent and are mostly triggered by torrential rain during Monsoon and Westerlies, leading to highway blockade. Rockfall and debris flow are prime mode of slope failures. Regional to site-specific approach was implemented to assess risk associated with these two modes. Remote sensing-based techniques were used to identify potential hazardous sites, which were further investigated for risk assessment. Modified Pierson’s rockfall hazard rating system (RHRS) rated potential rockfalls, whereas semi-quantitative technique was employed to assess debris flows. Normalized scores of each site shaped the final map, further classified into four zones: very high, high, intermediate and low risk.

scholarly journals A multivariate statistical method for susceptibility analysis of the debris flow in Southwest China

2019 ◽  
Author(s):  
Feng Ji ◽  
Zili Dai
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Loose Material ◽  
Southwest China ◽  
Quantitative Prediction ◽  
The Tibetan Plateau ◽  
Type I ◽  
Quantification Theory ◽  
Susceptibility Analysis ◽  
Dadu River

Abstract. Southwest China is characterized by many steep mountains and deep valleys due to the uplift activity of the Tibetan Plateau. The 2008 Wenchuan Earthquake left large amounts of loose materials in this area, making it a severe disaster zone in terms of debris flow. Susceptibility is a significant factor of debris flow for evaluating its formation and impact. Therefore, it is in urgent need to analyze the susceptibility of debris flows in this area. At present, the susceptibility analysis models of the debris flow in Southwest China is mainly based on qualitative methods. Little quantitative prediction model is found in the literature. This study evaluates 70 typical debris flow gullies as statistical samples, which are distributed along the Brahmaputra River, Nujiang River, Yalong River, Dadu River, and Ming River respectively. Nine indexes are chosen to construct a factor index system and then to evaluate the susceptibility of debris flow. They are the catchment area, longitudinal grade, average gradient of the slope on both sides of the gully, catchment morphology, valley slope orientation, loose material reserves, location of the main loose material, antecedent precipitation, and rainfall intensity. Then, an empirical model based on the quantification theory type I is established for the susceptibility prediction of debris flows in Southwest China. Finally, 10 debris flow gullies on the upstream of the Dadu River are analyzed to verify the reliability of the proposed model. The results show that the accuracy of the statistical model is 90 %.

scholarly journals Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway

2020 ◽  
Vol 12 (18) ◽  
pp. 2933
Author(s):  
Feng Qing ◽  
Yan Zhao ◽  
Xingmin Meng ◽  
Xiaojun Su ◽  
Tianjun Qi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Debris Flow ◽  
Debris Flows ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Safe Operation ◽  
Extreme Gradient Boosting ◽  
Karakoram Highway ◽  
Debris Flow Susceptibility

The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.

scholarly journals Debris Flow Assessment in the Gaizi-Bulunkou Section of Karakoram Highway

2021 ◽  
Vol 9 ◽  
Author(s):  
Ning Jiang ◽  
Fenghuan Su ◽  
Yong Li ◽  
Xiaojun Guo ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Regional Scale ◽  
Essential Information ◽  
Calculation Results ◽  
Karakoram Highway ◽  
Four Levels ◽  
Hazard Level ◽  
Debris Flow Hazard ◽  
Hazard Levels

Highways frequently run through the flow and accumulation areas of debris flow gullies and thus are susceptible to debris flow hazards. Assessing debris flows along highways can provide references for highway planners and debris flow control, emergency management. However, the existing assessment methods mostly neglect the essential information of the flow paths and spreading areas of debris flows at the regional scale. Taking the Gaizi Village-Bulunkou Township Section (hereinafter referred to as “the Gaizi-Bulunkou Section”) of the Karakoram highway as the study area, this research introduces a simple empirical model (the Flow-R model) and establishes a method for assessing the debris flow hazard level. The main processes include data collection, inventory of former events, calculating source areas and spreading probability, verification of the model, extraction of hazard assessment factors, and calculation of debris flow hazard levels. The results show that: 1) the accuracy, sensitivity, and positive predictive power of the Flow-R model in simulating the debris flow spreading probability of the study area were 81.87, 70.80 and 72.70%, respectively. The errors mainly occurred in the debris flow fans. 2) The calculation results make it possible to divide debris flow hazard levels into four levels. N5, N19, and N28 gullies had the highest hazard level during the study period. 3) In the Gaizi-Bulunkou Section of the Karakoram highway, during the study period, the highways with very high, high, medium, and low hazards were 4.33, 0.62, 1.41, and 1.68 km in length, respectively.

scholarly journals Analysis of the ground vibration produced by debris flows and other torrential processes at the Rebaixader monitoring site (Central Pyrenees, Spain)

2013 ◽  
Vol 1 (4) ◽  
pp. 4389-4423 ◽  
Author(s):  
C. Abancó ◽  
M. Hürlimann ◽  
J. Moya
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Ground Vibration ◽  
Monitoring Site ◽  
Site Specific ◽  
Flow Monitoring ◽  
Vibration Sensors ◽  
Specific Factors ◽  
Flow Detection ◽  
Central Pyrenees

Abstract. The use of ground vibration sensors for debris-flow monitoring has increased in the last two decades. However, the correct interpretation of the seismic signals produced by debris flows still presents many uncertainties. In the Rebaixader monitoring site (Central Pyrenees, Spain) two different ground vibration stations with different characteristics in terms of recording systems and site-specific factors have been compared. The shape of the time series has been recognised as one of the key parameters to identify events and to distinguish between different types of torrential processes. The results show that the site-specific factors strongly influence on the ground vibration registered at each geophone. The attenuation of the signal with the distance has been identified as linear to exponential. In addition, the assembly of the geophones to the terrain also has an important effect on the amplification of the signal. All these results highlight that the definition of ground vibration thresholds for debris-flow detection or warning purposes is a difficult task which is clearly influenced by site-specific conditions of the geophones.

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.

DEVELOPING AND SELECTING SLOPE STABILIZATION TECHNIQUES IN MANAGING SLOPE FAILURES

2016 ◽  
Vol 12 (4) ◽  
Author(s):  
Ari Sandyavitri
Keyword(s):  
Retaining Wall ◽  
Value Engineering ◽  
Rating Systems ◽  
Slope Stabilization ◽  
Rockfall Hazard ◽  
Proactive Approach ◽  
West Sumatra ◽  
Hazard Rating ◽  
Hazard Rating Systems ◽  
Steep Slopes

This paper objectives are to; (i) identification of risky slopes (within 4 Provinces in Sumatra including Provinces of Riau, West Sumatra, Jambi and South Sumatra encompassing 840 kms of the “Jalan Lintas Sumatra” highway) based on Rockfall Hazard Rating Systems (RHRS) method; (ii) developing alternatives to stabilize slope hazards, and (iii) selecting appropriate slopes stabilization techniques based on both proactive approach and value engineering one. Based on the Rockfall Hazard Rating Systems (RHRS) method, it was identified 109 steep slopes prone to failure within this highway section. Approximately, 15 slopes were identified as potential high-risk slopes (RHRS scores were calculated >200 points). Based on the proactive approach, seven riskiest slopes ware identified. The preferred stabilization alternatives to remedy most of these slopes are suggested as follow; either (i) a combination of retaining wall and drainage, or (ii) gabion structure and drainage. However, different approaches may yield different results, there are at least 2 main consideration in prioritizing slope stabilization; (i) based on the riskiest slopes, and(ii) the least expensive stabilization alternatives.

WHAT CAN WE LEARN FROM SEISMIC RECORDINGS OF DEBRIS FLOWS? PRELIMINARY FINDINGS FROM LARGE SCALE SEISMIC EXPERIMENTS AT THE USGS DEBRIS-FLOW FLUME

2016 ◽  
Author(s):  
Kate E. Allstadt ◽  
◽  
Maxime Farin ◽  
Jason W. Kean ◽  
Richard M. Iverson ◽  
...  
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Large Scale

scholarly journals Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 750
Author(s):  
Antonio Pasculli ◽  
Jacopo Cinosi ◽  
Laura Turconi ◽  
Nicola Sciarra
Keyword(s):  
Debris Flow ◽  
Shallow Water ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning System ◽  
Warning System ◽  
Extreme Weather Events ◽  
Water Model ◽  
Computational Time ◽  
Processing Unit

The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows.

scholarly journals Numerical Simulation of Non-Homogeneous Viscous Debris-Flows Based on the Smoothed Particle Hydrodynamics (SPH) Method

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2314 ◽  
Author(s):  
Shu Wang ◽  
Anping Shu ◽  
Matteo Rubinato ◽  
Mengyao Wang ◽  
Jiping Qin
Keyword(s):  
Debris Flow ◽  
Water Content ◽  
Smoothed Particle Hydrodynamics ◽  
Debris Flows ◽  
Sph Method ◽  
Particle Hydrodynamics ◽  
Sph Model ◽  
Smoothed Particle ◽  
The Impact ◽  
Kinetic And Potential Energies

Non-homogeneous viscous debris flows are characterized by high density, impact force and destructiveness, and the complexity of the materials they are made of. This has always made these flows challenging to simulate numerically, and to reproduce experimentally debris flow processes. In this study, the formation-movement process of non-homogeneous debris flow under three different soil configurations was simulated numerically by modifying the formulation of collision, friction, and yield stresses for the existing Smoothed Particle Hydrodynamics (SPH) method. The results obtained by applying this modification to the SPH model clearly demonstrated that the configuration where fine and coarse particles are fully mixed, with no specific layering, produces more fluctuations and instability of the debris flow. The kinetic and potential energies of the fluctuating particles calculated for each scenario have been shown to be affected by the water content by focusing on small local areas. Therefore, this study provides a better understanding and new insights regarding intermittent debris flows, and explains the impact of the water content on their formation and movement processes.

The Debris Flow Disaster Faster-Than-Early Forecast System (DFS) with Multi-Sensors Integrated Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 975-979
Author(s):  
Rong Zhao ◽  
Cai Hong Li ◽  
Yun Jian Tan ◽  
Jun Shi ◽  
Fu Qiang Mu ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Debris Flow ◽  
Real Time ◽  
Debris Flows ◽  
Base Station ◽  
Base Stations ◽  
Wireless Sensor ◽  
Forecast System ◽  
Debris Flow Disaster

This paper presents a Debris Flow Disaster Faster-than-early Forecast System (DFS) with wireless sensor networks. Debris flows carrying saturated solid materials in water flowing downslope often cause severe damage to the lives and properties in their path. Faster-than-early or faster-than-real-time forecasts are imperative to save lives and reduce damage. This paper presents a novel multi-sensor networks for monitoring debris flows. The main idea is to let these sensors drift with the debris flow, to collect flow information as they move along, and to transmit the collected data to base stations in real time. The Raw data are sent to the cloud processing center from the base station. And the processed data and the video of the debris flow are display on the remote PC. The design of the system address many challenging issues, including cost, deployment efforts, and fast reaction.

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