scholarly journals DebrisFlow Predictor: an agent-based runout program for shallow landslides

2021 ◽  
Vol 21 (3) ◽  
pp. 1029-1049
Author(s):  
Richard Guthrie ◽  
Andrew Befus
Keyword(s):  
Predictive Model ◽  
Debris Flows ◽  
Flow Behavior ◽  
Regional Scale ◽  
Path Selection ◽  
Agent Based ◽  
Mountainous Regions ◽  
Debris Avalanches ◽  
First Case ◽  
Landslide Runout

Abstract. Credible models of landslide runout are a critical component of hazard and risk analysis in the mountainous regions worldwide. Hazard analysis benefits enormously from the number of available landslide runout models that can recreate events and provide key insights into the nature of landsliding phenomena. Regional models that are easily employed, however, remain a rarity. For debris flows and debris avalanches, where the impacts may occur some distance from the source, there remains a need for a practical, predictive model that can be applied at the regional scale. We present, herein, an agent-based simulation for debris flows and debris avalanches called DebrisFlow Predictor. A fully predictive model, DebrisFlow Predictor employs autonomous subroutines, or agents, that act on an underlying digital elevation model (DEM) using a set of probabilistic rules for scour, deposition, path selection, and spreading behavior. Relying on observations of aggregate debris flow behavior, DebrisFlow Predictor predicts landslide runout, area, volume, and depth along the landslide path. The results can be analyzed within the program or exported in a variety of useful formats for further analysis. A key feature of DebrisFlow Predictor is that it requires minimal input data, relying primarily on a 5 m DEM and user-defined initiation zones, and yet appears to produce realistic results. We demonstrate the applicability of DebrisFlow Predictor using two very different case studies from distinct geologic, geomorphic, and climatic settings. The first case study considers sediment production from the steep slopes of Papua, the island province of Indonesia; the second considers landslide runout as it affects a community on Vancouver Island off the west coast of Canada. We show how DebrisFlow Predictor works, how it performs compared to real world examples, what kinds of problems it can solve, and how the outputs compare to historical studies. Finally, we discuss its limitations and its intended use as a predictive regional landslide runout tool. DebrisFlow Predictor is freely available for non-commercial use.

scholarly journals LABS: an agent-based run-out program for shallow landslides

2020 ◽  
Author(s):  
Richard Guthrie ◽  
Andrew Befus
Keyword(s):  
Predictive Model ◽  
Debris Flows ◽  
Flow Behavior ◽  
Regional Scale ◽  
Path Selection ◽  
Agent Based ◽  
Mountainous Regions ◽  
Debris Avalanches ◽  
First Case ◽  
Landslide Runout

Abstract. Credible models of landslide runout are a critical component of hazard and risk analysis in the mountainous regions worldwide. Hazard analysis benefits enormously from the number of available landslide runout models that can recreate events and provide key insights into the nature of landsliding phenomena. Regional models that are easily employed, however, remain a rarity. For debris flows and debris avalanches, where the impacts may occur some distance from the source, there remains a need for a practical, predictive model that can be applied at the regional scale. We present, herein, an agent-based simulation for debris flows and debris avalanches called LABS. A fully predictive model, LABS employs autonomous sub-routines, or agents, that act on an underlying DEM using a set of probabilistic rules for scour, deposition, path selection, and spreading behavior. Relying on observations of aggregate debris flow behavior, LABS predicts landslide runout, area, volume, and depth along the landslide path. The results can be analyzed within the program or exported in a variety of useful formats for further analysis. A key feature of LABS is that it requires minimal input data, relying primarily on a 5 m DEM and user defined initiation zones, and yet appears to produce realistic results. We demonstrate the applicability of LABS using two very different case studies from distinct geologic, geomorphic, and climatic settings. The first case study considers sediment production from the steep slopes of Papua, the island province of Indonesia; the second considers landslide runout as it affects a community on Vancouver Island off the west coast of Canada. We show how LABS works, how it performs compared to real world examples, what kinds of problems it can solve, and how the outputs compare to historical studies. Finally, we discuss its limitations and its intended use as a predictive regional landslide runout tool. LABS is freely available to not for profit groups including universities, NGOs and government organizations.

A probabilistic model for assessing debris flow propagation at regional scale: a case study in Campania region, Italy

2021 ◽  
Author(s):  
Luca Crescenzo ◽  
Gaetano Pecoraro ◽  
Michele Calvello ◽  
Richard Guthrie
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Regional Scale ◽  
Erosion And Deposition ◽  
Campania Region ◽  
Source Areas ◽  
Model Input ◽  
Travel Path ◽  
Debris Avalanches ◽  
Modelling Approach

<p>Debris flows and debris avalanches are rapid to extremely rapid landslides that tend to travel considerable distances from their source areas. Interaction between debris flows and elements at risk along their travel path may result in potentially significant destructive consequences. One of the critical challenges to overcome with respect to debris flow risk is, therefore, the credible prediction of their size, travel path, runout distance, and depths of erosion and deposition. To these purposes, at slope or catchment scale, sophisticated physically-based models, appropriately considering several factors and phenomena controlling the slope failure mechanisms, may be used. These models, however, are computationally costly and time consuming, and that significantly hinders their applicability at regional scale. Indeed, at regional scale, debris flows hazard assessment is usually carried out by means of qualitative approaches relying on field surveys, geomorphological knowledge, geometric features, and expert judgement.</p><p>In this study, a quantitative modelling approach based on cellular automata methods, wherein individual cells move across a digital elevation model (DEM) landscape following behavioral rules defined probabilistically, is proposed and tested. The adopted model, called LABS, is able to estimate erosion and deposition soil volumes along a debris flow path by deploying at the source areas autonomous subroutines, called agents, over a 5 m spatial resolution DEM, which provides the basic information to each agent in each time-step. Rules for scour and deposition are based on mass balance considerations and independent probability distributions defined as a function of slope DEM-derived values and a series of model input parameters. The probabilistic rules defined in the model are based on data gathered for debris flows and debris avalanches that mainly occurred in western Canada. This study mainly addresses the applicability and the reliability of this modelling approach to areas in southern Italy, in Campania region, historically affected by debris flows in pyroclastic soils. To this aim, information on inventoried debris flows is used in different study areas to evaluate the effect on the predictions of the model input parameter values, as well as of different native DEM resolutions.</p>

scholarly journals Description and analysis of the debris flows occurred during 2008 in the Eastern Pyrenees

2010 ◽  
Vol 10 (7) ◽  
pp. 1635-1645 ◽  
Author(s):  
M. Portilla ◽  
G. Chevalier ◽  
M. Hürlimann
Keyword(s):  
Debris Flows ◽  
Regional Scale ◽  
Slope Angle ◽  
Large Mass ◽  
Rainfall Data ◽  
Published Data ◽  
Mountainous Regions ◽  
Initiation Zone ◽  
Eastern Pyrenees ◽  
Fluid Rheology

Abstract. Rainfall-triggered landslides taking place in the Spanish Eastern Pyrenees have usually been analysed on a regional scale. Most research focussed either on terrain susceptibility or on the characteristics of the critical rainfall, neglecting a detailed analysis of individual events. In contrast to other mountainous regions, research on debris flow has only been performed marginally and associated hazard has mostly been neglected. In this study, five debris flows, which occurred in 2008, are selected; and site specific descriptions and analysis regarding geology, morphology, rainfall data and runout were performed. The results are compared with worldwide data and some conclusions on hazard assessment are presented. The five events can be divided into two in-channel debris flows and three landslide-triggered debris flows. The in-channel generated debris flows exceeded 10 000 m3, which are unusually large mass movements compared to historic events which occurred in the Eastern Pyrenees. In contrast, the other events mobilised total volumes less than 2000 m3. The geomorphologic analysis showed that the studied events emphasize similar patterns when compared to published data focussing on slope angle in the initiation zone or catchment area. Rainfall data revealed that all debris flows were triggered by high intensity-short duration rainstorms during the summer season. Unfortunately, existing rainfall thresholds in the Eastern Pyrenees consider long-lasting rainfall, usually occurring in autumn/winter. Therefore, new thresholds should be established taking into account the rainfall peak intensity in mm/h, which seems to be a much more relevant factor for summer than the event's total precipitation. The runout analysis of the 2008 debris flows confirms the trend that larger volumes generally induce higher mobility. The numerical simulation of the Riu Runer event shows that its dynamic behaviour is well represented by Voellmy fluid rheology. A maximum front velocity of 7 m/s was back-analysed for the transit section and even on the fan velocities larger than 2 m/s were obtained. This preliminary analysis of the major Eastern Pyrenean debris flows represents the first background for future studies. Additional research on other events is necessary to support the results presented herein, and to properly assess and reduce hazard related to debris flows.

Forecast of COVID-19 Cases in Nigeria: An implementation of an ARIMA model (Preprint)

2020 ◽  
Author(s):  
Sanyaolu Ameye ◽  
Michael Awoleye ◽  
Emmanuel Agogo ◽  
Ette Etuk
Keyword(s):  
Predictive Model ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
Series Data ◽  
Global Pandemic ◽  
First Case ◽  
Epidemiological Trend ◽  
Data Points

BACKGROUND The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. Though, the disease is just over three months since first case was identified in the country, we present a predictive model to forecast the number of cases expected to be seen in the country in the next 100 days. OBJECTIVE To implement a predictive model in forecasting the near future number of positive cases expected in the country following the present trend METHODS We performed an Auto Regressive Integrated Moving Average (ARIMA) model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. RESULTS There were 93 time series data points which lacked stationarity. From our ARIMA model, it is expected that the number of new cases declared per day will keep rising and towards the early September, 2020, Nigeria is expected to have well above sixty thousand confirmed cases. CONCLUSIONS We however believe that as we have more data points our model will be better fine-tuned.

scholarly journals Influence of biopolymers on the rheological properties of seafloor sediments and the runout behavior of submarine debris flows

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Kameda ◽  
Hamada Yohei
Keyword(s):  
Rheological Properties ◽  
Debris Flows ◽  
Extracellular Polymeric Substances ◽  
Flow Behavior ◽  
Mass Movement ◽  
Rheological Parameters ◽  
Natural Environments ◽  
Natural Systems ◽  
Chemical Conditions ◽  
Seafloor Sediments

AbstractSubmarine debris flows are mass movement processes on the seafloor, and are geohazards for seafloor infrastructure such as pipelines, communication cables, and submarine structures. Understanding the generation and run-out behavior of submarine debris flows is thus critical for assessing the risk of such geohazards. The rheological properties of seafloor sediments are governed by factors including sediment composition, grain size, water content, and physico-chemical conditions. In addition, extracellular polymeric substances (EPS) generated by microorganisms can affect rheological properties in natural systems. Here we show that a small quantity of EPS (~ 0.1 wt%) can potentially increase slope stability and decrease the mobility of submarine debris flows by increasing the internal cohesion of seafloor sediment. Our experiments demonstrated that the flow behavior of sediment suspensions mixed with an analogue material of EPS (xanthan gum) can be described by a Herschel–Bulkley model, with the rheological parameters being modified progressively, but not monotonously, with increasing EPS content. Numerical modeling of debris flows demonstrated that the run-out distance markedly decreases if even 0.1 wt% of EPS is added. The addition of EPS can also enhance the resistivity of sediment to fluidization triggered by cyclic loading, by means of formation of an EPS network that binds sediment particles. These findings suggest that the presence of EPS in natural environments reduces the likelihood of submarine geohazards.

scholarly journals Simulating Spatio-Temporal Patterns of Bicycle Flows with an Agent-Based Model

2021 ◽  
Vol 10 (2) ◽  
pp. 88
Author(s):  
Dana Kaziyeva ◽  
Martin Loidl ◽  
Gudrun Wallentin
Keyword(s):  
Regional Scale ◽  
Evidence Base ◽  
Wide Field ◽  
Traffic Patterns ◽  
Agent Based Model ◽  
Validation Data ◽  
Agent Based ◽  
Mobility Behavior ◽  
Spatio Temporal ◽  
Models Validation

Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model’s level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion

Development of an initiation criterion for debris flows based on local topographic properties and applicability assessment at a regional scale

2017 ◽  
Vol 230 ◽  
pp. 64-76 ◽  
Author(s):  
Sinhang Kang ◽  
Seung-Rae Lee ◽  
Nikhil N. Vasu ◽  
Joon-Young Park ◽  
Deuk-Hwan Lee
Keyword(s):  
Debris Flows ◽  
Regional Scale

scholarly journals Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

2021 ◽  
Vol 10 (5) ◽  
pp. 315
Author(s):  
Hilal Ahmad ◽  
Chen Ningsheng ◽  
Mahfuzur Rahman ◽  
Md Monirul Islam ◽  
Hamid Reza Pourghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Debris Flows ◽  
Regional Scale ◽  
Indus Basin ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Susceptibility Maps ◽  
Landslides And Debris Flows

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

scholarly journals Empirical atmospheric thresholds for debris flows and flash floods in the southern French Alps

2014 ◽  
Vol 14 (6) ◽  
pp. 1517-1530 ◽  
Author(s):  
T. Turkington ◽  
J. Ettema ◽  
C. J. van Westen ◽  
K. Breinl
Keyword(s):  
Debris Flows ◽  
Warning System ◽  
Flash Floods ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Atmospheric Conditions ◽  
Convective Rainfall ◽  
French Alps ◽  
Mountainous Regions

Abstract. Debris flows and flash floods are often preceded by intense, convective rainfall. The establishment of reliable rainfall thresholds is an important component for quantitative hazard and risk assessment, and for the development of an early warning system. Traditional empirical thresholds based on peak intensity, duration and antecedent rainfall can be difficult to verify due to the localized character of the rainfall and the absence of weather radar or sufficiently dense rain gauge networks in mountainous regions. However, convective rainfall can be strongly linked to regional atmospheric patterns and profiles. There is potential to employ this in empirical threshold analysis. This work develops a methodology to determine robust thresholds for flash floods and debris flows utilizing regional atmospheric conditions derived from ECMWF ERA-Interim reanalysis data, comparing the results with rain-gauge-derived thresholds. The method includes selecting the appropriate atmospheric indicators, categorizing the potential thresholds, determining and testing the thresholds. The method is tested in the Ubaye Valley in the southern French Alps (548 km2), which is known to have localized convection triggered debris flows and flash floods. This paper shows that instability of the atmosphere and specific humidity at 700 hPa are the most important atmospheric indicators for debris flows and flash floods in the study area. Furthermore, this paper demonstrates that atmospheric reanalysis data are an important asset, and could replace rainfall measurements in empirical exceedance thresholds for debris flows and flash floods.

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