scholarly journals A new approach to mapping landslide hazards: a probabilistic integration of empirical and process-based models in the North Cascades of Washington, U.S.A.

2019 ◽  
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel
Keyword(s):  
Statistical Approach ◽  
Landslide Hazard ◽  
Ratio Method ◽  
North Cascades ◽  
New Approach ◽  
Source Areas ◽  
Depositional Processes ◽  
Debris Avalanches ◽  
Landslide Hazards ◽  
Infinite Slope Stability Model

Abstract. We developed a new approach for mapping landslide hazard by combining probabilities of landslide impact derived from a data-driven statistical approach and process-based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes on observed landslides using a frequency ratio method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the capture of different landslide processes or components, which relate to different landslide-inducing factors. Slopes greater than 35° are more frequently associated with landslide initiation, while higher landslide hazards at gentler slopes (< 30°) reflect depositional processes from observations of all landslide types or debris avalanches. Source areas are associated with mid to high elevations (1,400 to 1,800 m), where they are linked to ecosystem transition (e.g., forest to barren), while all landslides types and debris avalanches show increasing frequency in lower elevations (< 1,200 m). Slope is a key attribute in the initiation of landslides, while lithology is mainly linked to transport and depositional processes. East (west) aspect is a positive (negative) landslide-influencing factor, likely due to differences in forest cover and associated root cohesion, and evapotranspiration. The empirical model probability derived from debris avalanche source area is combined probabilistically with a previously developed processed-based probabilistic model to produce an integrated probability of landslide hazard for initiation that includes mechanisms not captured by the infinite slope stability model. We apply our approach in North Cascades National Park Complex in Washington, USA, to provide multiple landslide hazard maps that land managers can use for planning and decision making, as well as educating the public about hazards from landslides in this remote high-relief terrain.

scholarly journals A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

2019 ◽  
Vol 19 (11) ◽  
pp. 2477-2495
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel
Keyword(s):  
Statistical Approach ◽  
Joint Probability ◽  
North Cascades ◽  
Physically Based Model ◽  
New Approach ◽  
The North ◽  
Debris Avalanches ◽  
Landslide Hazards ◽  
Infinite Slope Stability Model ◽  
Physically Based

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.

GIS-based landslide hazard assessment: an overview

2005 ◽  
Vol 29 (4) ◽  
pp. 548-567 ◽  
Author(s):  
Wang Huabin ◽  
Liu Gangjun ◽  
Xu Weiya ◽  
Wang Gonghui
Keyword(s):  
Hazard Assessment ◽  
Landslide Hazard ◽  
Integrated System ◽  
Process Models ◽  
Land Utilization ◽  
Property A ◽  
Landslide Hazard Assessment ◽  
Landslide Hazards ◽  
Digital Elevation ◽  
Slope Management

In recent years, landslide hazard assessment has played an important role in developing land utilization regulations aimed at minimizing the loss of lives and damage to property. A variety of approaches has been used in landslide assessment and these can be classified into qualitative factor overlay, statistical models, geotechnical process models, etc. However, there is little work on the satisfactory integration of these models with geographic information systems (GIS) to support slope management and landslide hazard mitigation. This paper deals with several aspects of landslide hazard assessment by presenting a focused review of GIS-based landslide hazard assessment: it starts with a framework for GIS-based assessment of landslide hazard; continues with a critical review of the state of the art in using GIS and digital elevation models (DEM) for mapping and modelling landslide hazards; and concludes with a description of an integrated system for effective landslide hazard assessment and zonation incorporating artificial intelligence and data mining technology in a GIS-based framework of knowledge discovery.

scholarly journals Measuring Housing Affordability Using Residual Income Method for Million-plus Cities in India

2021 ◽  
Author(s):  
Prabhat Kumar Rao ◽  
◽  
Arindam Biswas ◽  
Keyword(s):  
Affordable Housing ◽  
Poverty Line ◽  
Housing Affordability ◽  
Residual Income ◽  
Ratio Method ◽  
New Approach ◽  
Policies And Programs ◽  
The City ◽  
Indian Cities ◽  
Living Cost

Housing affordability is an ever-growing concern in rapidly urbanizing countries like India. The need for affordable housing can hardly be overemphasis in India. Government has many policies and programs running for fulfilling the requirement. But it is essential to define affordability standards for the success of any such policies and programs. The Ratio Method, which is currently used as the base for determining affordable housing, doesn’t have the flexibility to match the varied scale and standards across Indian cities. This paper is based on Michel E stone’s residual income’ method to measure housing affordability for India’s million-plus city. It gives a new approach for measuring housing affordability based on the minimum living cost for survival. It uses Poverty Line data (2014) and NSSO economic survey data (2012) for defining the minimum standard of living in the city. Stakeholders can use the city-specific measurement for affordable housing generated from this paper in affordable housing policies and programs.

scholarly journals Identifikasi Bahaya Longsor Lahan di Sebagian Wilayah Poncokusumo dan Wajak Kabupaten Malang

2020 ◽  
Vol 4 (2) ◽  
pp. 160-171
Author(s):  
Listyo Yudha Irawan ◽  
Keyword(s):  
Landslide Hazard ◽  
National Standard ◽  
Land Uses ◽  
Forest Land ◽  
Scoring Method ◽  
Landslide Hazards ◽  
Geological Conditions ◽  
Hazard Level ◽  
Moderate Hazard ◽  
Hazard Levels

Poncokusumo and Wajak regions are one of the Districts in Malang Regency. Poncokusumo and Wajak have varied land uses, geology and morphogenesis. This physiographic condition has an effect on the increasing threat of landslides. This study aims to determine the level of landslide hazard and its distribution. The method used in the identification of landslide hazards is the scoring method which refers to the 2012 BNPB Head Regulation, Indonesian National Standard number 13-7124-2005, the Indirect Method, and the Indonesian Disaster Risk (RBI) BNPB 2016. The results showed that the geological conditions of the study area were composed of volcanic materials such as lava and lava deposits. This material is loose and unstable. Based on the slope classification, this area consists of flat areas with a slope of 0-8% to steep areas with a slope of> 40%. Based on the morphological conditions, it can be seen that this area is an area prone to landslides. Landslide hazard levels in parts of Poncokusumo and Wajak are low and medium. Low landslide hazard levels are dominated by forest land use. The level of danger of a lonsor is being dominated by the use of residential land. The area with a low hazard level is 860.8 Ha and the area with a moderate hazard level is 365.1 Ha. Keywords: landslide, hazard, GIS

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

scholarly journals Drainage Basin of Lake Kivu in the Western Rift of East Africa and Hazards Potential

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
C. Kakonkwe ◽  
D. E. Rwabuhungu ◽  
M. Biryabarema
Keyword(s):  
Water Supply ◽  
Drainage Basin ◽  
Landslide Hazard ◽  
Mass Wasting ◽  
Landslide Hazards ◽  
Digital Elevation ◽  
Lake Kivu ◽  
Elevation Data ◽  
Digital Elevation Data ◽  
Land Sliding

A series of ArcGIS-generated maps were applied in analysing the potential for flooding and landslide hazards within the Lake Kivu drainage basin. This study was carried out using digital elevation data of the basin. The Kivu drainage basin encompasses an area of 7,382 km2. Sediment and water supply to Lake Kivu originate mostly from its eastern hinterland. The distribution of land sliding potentiality in the drainage basin shows that the northern and the southern portions of the basin are the ones with relatively low risk of land sliding, whereas the rift shoulders are most prone to land sliding. Mass wasting on slopes has the potential to grade downstream into debris and mudflows, promoting in turn further erosion and flooding. Keywords: drainage, Kivu, Africa, flooding, landslide, hazard

scholarly journals A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy

2002 ◽  
Vol 2 (1/2) ◽  
pp. 57-72 ◽  
Author(s):  
M. Cardinali ◽  
P. Reichenbach ◽  
F. Guzzetti ◽  
F. Ardizzone ◽  
G. Antonini ◽  
...  
Keyword(s):  
Central Italy ◽  
Landslide Hazard ◽  
Aerial Photographs ◽  
Site Specific ◽  
Landslide Hazards ◽  
Umbria Region ◽  
Multi Temporal ◽  
Landslide Inventory Map ◽  
Hazard And Risk ◽  
Inventory Map

Abstract. We present a geomorphological method to evaluate landslide hazard and risk. The method is based on the recognition of existing and past landslides, on the scrutiny of the local geological and morphological setting, and on the study of site-specific and historical information on past landslide events. For each study area a multi-temporal landslide inventory map has been prepared through the interpretation of various sets of stereoscopic aerial photographs taken over the period 1941–1999, field mapping carried out in the years 2000 and 2001, and the critical review of site-specific investigations completed to solve local instability problems. The multi-temporal landslide map portrays the distribution of the existing and past landslides and their observed changes over a period of about 60 years. Changes in the distribution and pattern of landslides allow one to infer the possible evolution of slopes, the most probable type of failures, and their expected frequency of occurrence and intensity. This information is used to evaluate landslide hazard, and to estimate the associated risk. The methodology is not straightforward and requires experienced geomorphologists, trained in the recognition and analysis of slope processes. Levels of landslide hazard and risk are expressed using an index that conveys, in a simple and compact format, information on the landslide frequency, the landslide intensity, and the likely damage caused by the expected failure. The methodology was tested in 79 towns, villages, and individual dwellings in the Umbria Region of central Italy.

scholarly journals The Tiskilwa Till, a Regional View of its Origin and Depositional Processes

1981 ◽  
Vol 2 ◽  
pp. 176-182 ◽  
Author(s):  
Susan Specht Wickham ◽  
W. Hilton Johnson
Keyword(s):  
Thermal Regime ◽  
Controlling Factor ◽  
Local Source ◽  
Source Areas ◽  
Depositional Processes ◽  
Red Color ◽  
Major Controlling Factor ◽  
Late Wisconsinan

The Tiskilwa Till Member of the Wedron Formation represents deposition by basal melt-outin the marginal area of the Laurentide ice sheetduring the Woodfordian (late-Wisconsinan) in Illinois. Distinctive characteristics include: a very thick, homogeneous till; relatively little ablation till; red color; sandy texture; illite content that is relatively low withrespect to other Woodfordian tills; and the presence of discontinuous basal zones of differing composition.Erosion and entrainment of debris from both distant and local source areas are evident in the Tiskilwa Jill. Basal thermal regime is suggested as a major controlling factor on the location of the zones of entrainment. The debris was homogenized en route to the margin and eventually was deposited as basal melt-out till near the margin. Deposition occurred within an interval of 6 ka or more during the first half of the Woodfordian.

Using Uncertain DM-Chameleon Clustering Algorithm Based on Machine Learning to Predict Landslide Hazards

2019 ◽  
Vol 31 (2) ◽  
pp. 329-338 ◽  
Author(s):  
Jian Hu ◽  
Haiwan Zhu ◽  
Yimin Mao ◽  
Canlong Zhang ◽  
Tian Liang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Uncertain Data ◽  
Landslide Hazard ◽  
Data Sets ◽  
Large Scale Data ◽  
Landslide Hazards ◽  
Hazard Levels ◽  
Scale Data

Landslide hazard prediction is a difficult, time-consuming process when traditional methods are used. This paper presents a method that uses machine learning to predict landslide hazard levels automatically. Due to difficulties in obtaining and effectively processing rainfall in landslide hazard prediction, and to the existing limitation in dealing with large-scale data sets in the M-chameleon algorithm, a new method based on an uncertain DM-chameleon algorithm (developed M-chameleon) is proposed to assess the landslide susceptibility model. First, this method designs a new two-phase clustering algorithm based on M-chameleon, which effectively processes large-scale data sets. Second, the new E-H distance formula is designed by combining the Euclidean and Hausdorff distances, and this enables the new method to manage uncertain data effectively. The uncertain data model is presented at the same time to effectively quantify triggering factors. Finally, the model for predicting landslide hazards is constructed and verified using the data from the Baota district of the city of Yan’an, China. The experimental results show that the uncertain DM-chameleon algorithm of machine learning can effectively improve the accuracy of landslide prediction and has high feasibility. Furthermore, the relationships between hazard factors and landslide hazard levels can be extracted based on clustering results.

Sign in / Sign up

Export Citation Format

Share Document