Topographic characteristics of rainfall triggered landslides from a newly compiled set of inventories

2021 ◽  
Author(s):  
Robert Emberson ◽  
Dalia Kirschbaum ◽  
Pukar Amatya ◽  
Hakan Tanyas ◽  
Odin Marc
Keyword(s):  
Machine Learning Techniques ◽  
Clear Understanding ◽  
Saturation State ◽  
Wetness Index ◽  
Mountainous Regions ◽  
Topographic Roughness ◽  
High Resolution Imagery ◽  
Modelling Studies ◽  
Position Index ◽  
Topographic Characteristics

<p>Landslides triggered by rainfall or seismic activity are a significant source of loss of life and property damage in mountainous regions. In these settings, it is critical to plan development and infrastructure to avoid impact from landslides. To do so, it is necessary to have a clear understanding of the topographic characteristics of areas both where landslides are initially triggered but also the down-slope areas where debris and bedrock fragments are deposited. Recent research has investigated the characteristics of landslide locations triggered by seismic motion, providing guidelines about the most hazardous parts of a given landscape. In this contribution, we report on a set of analyses conducted on a large compilation of landslide inventories associated with major rainfall events around the world. This compilation includes a number of previously published inventories together with 6 newly mapped inventories of landslides created using high-resolution imagery and machine learning techniques. To our knowledge, together these form the most comprehensive compilation of rainfall triggered landslide inventories gathered to date.</p><p>We analyse a number of topographic characteristics associated with these landslides using the 30m resolution SRTM DEM, including local slope, average upstream slope, relief, topographic roughness, wetness index, and topographic position index. We analyse these parameters for both the scar of the landslide as well as the area of deposition. While there is significant dispersion across inventories for several of these parameters, there are consistent relationships between landslide likelihood and roughness, slope, and wetness index. Although the relationships identified with slope and roughness are consistent with prior work, the relationship between wetness index and landslide likelihood suggests that the calculation of wetness index from topography alone may not effectively represent the saturation state of the hillslopes. We anticipate that these findings could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment.</p>

scholarly journals Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories

2021 ◽  
Author(s):  
Robert Emberson ◽  
Dalia Kirschbaum ◽  
Pukar Amatya ◽  
Hakan Tanyas ◽  
Odin Marc
Keyword(s):  
Inventory Data ◽  
Topographic Position Index ◽  
Landslide Event ◽  
Modelling Studies ◽  
Position Index ◽  
Local Relief ◽  
Topographic Characteristics ◽  
Triggering Events ◽  
Compound Topographic Index ◽  
Different Parts

Abstract. Landslides are a key hazard in high-relief areas around the world and pose a risk to population and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that average upstream angle and compound topographic index are strong predictors of landslide headscarp location, while local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.

Morphometrical analysis of Debris flow fans and torrential catchments in mountainous terrain in the northern Colombian Andes by machine learning techniques.

2021 ◽  
Author(s):  
Emanuel Castillo Cardona ◽  
Edier Aristizábal
Keyword(s):  
Machine Learning ◽  
Debris Flow ◽  
Land Use Planning ◽  
Machine Learning Techniques ◽  
Main Stream ◽  
Mountain Range ◽  
Mountainous Regions ◽  
Colombian Andes ◽  
Learning Techniques ◽  
Qualitative Variables

<p>Debris flow fans are commonly occupied by urban and rural settlements in mountainous regions such as in the northern Colombian Andes. Those fans are originated by violent surges of high sediment concentration that are then mobilized downstream by strong currents during torrential events highly destructive. Then, characterization and understanding of the dynamics that give rise to fans in tropical and mountainous regions such as Andean zone is a fundamental tool for land use planning. This research focuses on cartography of fans and catchments using digital elevation models in the central and western mountain range of the northern part of the Andean mountain belt. The methodology considered: morphometric measurements of the catchments and fans, lithological aspects of the catchments, type of catchments (torrential or no torrential). Then the correlation between morphometric parameters of fans and catchments is carried out, including relationships with qualitative variables by multivariate statistical analysis and machine learning techniques to find patterns between quantitative and qualitative variables. The results indicate that slope of the fans has a high correlation with Melton index of the catchments and with the slope of the main stream of the catchments. About the qualitative classification of the catchments in torrential and no torrential, it is observed that there are good discriminations for slope of the fan, volume of the deposits(fans), the relationship between the relief of the catchments and other variables. On the other hand, the lithology of the catchments does not have strong influences on the morphometry of the fans.</p>

scholarly journals Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece.

2016 ◽  
Vol 47 (1) ◽  
pp. 264 ◽  
Author(s):  
I. Ilia ◽  
D. Rozos ◽  
I. Koumantakis
Keyword(s):  
Spatial Analysis ◽  
Power Index ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Topographic Position Index ◽  
Landform Classification ◽  
Essential Parameter ◽  
Position Index ◽  
Stream Power Index

The main objective of this paper is to classify landforms in Kimi municipality area of Euboea Island, Greece using advanced spatial techniques. Landform categories were determined by conducting morphometric analysis through the use of advanced GIS functions. In particular, the process of classifying the landscape into landform categories was based on Topographic Position Index (TPI). The main topographic elements such as slope inclination, aspect, slope shape (curvature), topographic wetness index and stream power index were obtained from the DEM file of the study area. Landform classification was obtained using TPI grids and the classes were related with the geological pattern and the land cover by sophisticated spatial analysis function. The knowledge obtained from the present study could be useful in identifying areas prone to land degradation and instability problems in which landforms are identified as an essential parameter

scholarly journals Automated Cobble Mapping of a Mixed Sand-Cobble Beach Using a Mobile LiDAR System

2018 ◽  
Vol 10 (8) ◽  
pp. 1253 ◽  
Author(s):  
Hironori Matsumoto ◽  
Adam Young
Keyword(s):  
Maximum Likelihood ◽  
Large Scale ◽  
Spatial Location ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Lidar System ◽  
K Nearest Neighbors ◽  
Terrestrial Lidar ◽  
Topographic Roughness ◽  
Cobble Beach

Cobbles (64–256 mm) are found on beaches throughout the world, influence beach morphology, and can provide shoreline stability. Detailed, frequent, and spatially large-scale quantitative cobble observations at beaches are vital toward a better understanding of sand-cobble beach systems. This study used a truck-mounted mobile terrestrial LiDAR system and a raster-based classification approach to map cobbles automatically. Rasters of LiDAR intensity, intensity deviation, topographic roughness, and slope were utilized for cobble classification. Four machine learning techniques including maximum likelihood, decision tree, support vector machine, and k-nearest neighbors were tested on five raster resolutions ranging from 5–50 cm. The cobble mapping capability varied depending on pixel size, classification technique, surface cobble density, and beach setting. The best performer was a maximum likelihood classification using 20 cm raster resolution. Compared to manual mapping at 15 control sites (size ranging from a few to several hundred square meters), automated mapping errors were <12% (best fit line). This method mapped the spatial location of dense cobble regions more accurately compared to sparse and moderate density cobble areas. The method was applied to a ~40 km section of coast in southern California, and successfully generated temporal and spatial cobble distributions consistent with previous observations.

scholarly journals Hubungan Karakteristik Topografi dengan Sifat-Sifat Fisika Tanah Studi Kasus: Sub DAS Citarik, DAS Citarum Hulu

SoilREns ◽  
2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Ade Setiawan ◽  
Mahfud Arifin ◽  
Rachmat Harryanto ◽  
Apong Sandrawati
Keyword(s):  
Physical Properties ◽  
Soil Bulk Density ◽  
Soil Physical Properties ◽  
Distribution Model ◽  
Topographic Wetness Index ◽  
Soil Diversity ◽  
Research Areas ◽  
Porosity And Permeability ◽  
Position Index ◽  
Topographic Characteristics

The proper understanding about spatial soil diversity is very important to simulate environmental model and to manage land resources in the landscape scale. Information of soil diversity is also noteworthy for environmental academics, forestry, civil engineering, and land use planner. Until now, most of the soil information are derived from conventional soil maps which are lack of detailed information. This condition can increase the uncertainty of model output and can also be an obstacle to the future development of spatial distribution model. According to this situation, the research was conducted in Sub Watershed of Cikeruh, Citarik, and Citarum Hulu. These areas are located in the 6o53’00”S - 6o53’15” S and 107o45’21”E - 107o45’55”E at 780-1800 m asl, The rainfall is classified as type C according to the Schmidt and Fergusson classification with mean rainfall around 1795.66 mm per year. The soils in the research areas are classified as Inceptisols.  The research aims to analyze and elucidate the relation between topographic characteristics and soil physical properties. Some parameters studied in this research are elevation, aspect, plan curvature, profile curvature, topographic wetness index (TWI), topographic position index (TPI), and some soil physical properties, such as soil texture (clay, silt, and sand), soil organic carbon, soil bulk density, soil available water capacity, soil porosity and permeability. The results showed that all topographic parameters were related to one or some soil physical properties, except aspect and slope. Topographic variables that are frequently related to soil properties are TPI and TWI. Keywords: topographic characteristics, elevation, aspect, curvature, TWI, TPI, soil physical properties

scholarly journals Landform clarification using automated Techniques in Geographical Information systems

2020 ◽  
Author(s):  
Ismael Abdulrahman Ismael Abdulrahman Abdulrahman
Keyword(s):  
Geographical Information Systems ◽  
Spatial Scales ◽  
Capital City ◽  
Geographical Information ◽  
Mountainous Regions ◽  
Topographic Position Index ◽  
Position Index ◽  
Elevation Model

Topographic position index (TPI) contain one of the most important algorithms that is used in GISenvironment forautomatelandform classificationsto obtaining an accurate spatial layers that represent physical featuresin reality.This study aims to determine the importance and role of the algorithm in identifyinglandform classification in mountainous areas.Duhok district selected as the case study which is the capital city of Duhokgovernorate, Iraqi Kurdistan region.Digital elevation model (DEM) with the spatial resolution of (30) meterswas employed, using two type of algorithms (Traditional TPI) and (Standardized Elevation)with different spatial scales(500, 1500, 3000, 6000) meters.The resultsillustrated that; there aresixmain types of landformsmost of them areedges and steep slope. As well, the proportions of these types vary according to the variation of indicator valuesin the index.The study showed that this technique play a powerful role in providing accurate results in landformclassification in mountainous regions compared to traditional methods

scholarly journals Quantifying the glacial meltwater contribution to streams in mountainous regions using highly resolved stable water isotope measurements

2021 ◽  
Author(s):  
Philipp Wanner ◽  
Noemi Buri ◽  
Kevin Wyss ◽  
Andreas Zischg ◽  
Rolf Weingartner ◽  
...  
Keyword(s):  
Energy Production ◽  
Swiss Alps ◽  
Glacial Meltwater ◽  
Mountainous Regions ◽  
Alpine Regions ◽  
Discharge Rates ◽  
The Future ◽  
Modelling Studies ◽  
Isotope Measurements ◽  
Water Isotope

Abstract. This study aims to determine the contribution of glacial meltwater to streams in mountainous regions based on stable water isotope measurements (δ18O and δ2H). For this purpose, three partially glaciated catchments were selected as the study area in the central Swiss Alps being representative of catchments that are used for hydropower energy production in Alpine regions. The glacial meltwater contribution to the catchments’ stream discharges was evaluated based on high-resolution δ18O and δ2H measurements of the end-members that contribute to the stream discharge (ice, rain, snow) and of the discharging streams. The glacial meltwater contribution to the stream discharges could be unequivocally quantified after the snowmelt in August and September when most of the annual glacial meltwater discharge occurs. In August and September, the glacial meltwater contribution to the stream discharges corresponds to up to 95 ± 2 % and to 28.7 % ± 5 % of the total annual discharge in the evaluated catchments. The high glacial meltwater contribution demonstrates that the mountainous stream discharges in August and September will probably strongly decrease in the future due to global warming-induced deglaciation, which will be, however, likely compensated by higher discharge rates in winter and spring. Nevertheless, the changing mountainous streamflow regimes in the future will pose a challenge for hydropower energy production in the mountainous areas. Overall, this study provides a successful example of an Alpine catchment monitoring strategy to quantify the glacial meltwater contribution to stream discharges based on stable isotope water data, which leads to a better validation of existing modelling studies and which can be adapted to other mountainous regions.

scholarly journals EPIGENETIC PROFILES OF ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S937-S937
Author(s):  
Kyra Thrush ◽  
Morgan E Levine
Keyword(s):  
Network Analysis ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Clear Understanding ◽  
Clinical Biomarkers ◽  
Age Related ◽  
Epigenetic Age ◽  
Lifestyle Choices ◽  
Level Information ◽  
Highly Correlated

Abstract Although age is highly correlated with incidence of Alzheimer’s Dementia (AD), the field continues to lack a clear understanding of how either normal and/or pathological aging processes drive neurodegeneration. As such, there remains a clear lack of valid and reliable clinical biomarkers to predict that disease’s future development and severity. Epigenetic age based on DNA methylation (DNAm) in brain have been shown to relate to AD neuropathology and cognitive decline. However, they were not initially designed as AD biomarkers. We hypothesized that supervised and unsupervised machine learning techniques (e.g. network analysis, clustering, and regressed-based techniques) could be used to build composite scoring variables from DNAm data that are predictive of AD progression. This work analyzes the methylation of 3 brain regions (cerebellum (CBM), prefrontal cortex (PFC), striatum (ST))—totaling 1,047 brain methylation samples. The samples contain neuropathologically confirmed AD cases and controls, and is enriched for APOE4+ carriers. Detailed subject-level information concerning cognitive measures, lifestyle choices, medications, and neuropathology at death were also considered. Based on epigenome-wide association study (EWAS), we identified a CpG in AIMP2 that is a robust predictor of AD-related phenotypes. Using network analysis, we have also identified co-methylation modules that relate to multifactorial AD phenotypes. Following validation, we intend to follow-up on the biological processes and molecular pathways associated with these epigenetic signatures. In moving forward, predictors of AD diagnosis and prognostication have major implications for early detection and treatment of this major age-related disease.

scholarly journals Topographic drivers of flight altitude over large spatial and temporal scales

The Auk ◽  
2019 ◽  
Vol 136 (2) ◽  
Author(s):  
Adam E Duerr ◽  
Tricia A Miller ◽  
Leah Dunn ◽  
Douglas A Bell ◽  
Peter H Bloom ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Ground Level ◽  
Spatial And Temporal Variation ◽  
Flight Altitude ◽  
Spatial And Temporal Scales ◽  
The North ◽  
Topographic Roughness ◽  
Topographic Characteristics ◽  
Soaring Flight ◽  
East West

Abstract Bird movements vary spatially and temporally, but the primary drivers that explain such variation can be difficult to identify. For example, it is well known that the availability of updraft influences soaring flight and that topography interacts with weather to produce these updrafts. However, the influences of topography on flight are not well understood. We determined how topographic characteristics influenced flight altitude above ground level (AGL) of a large soaring bird, the Golden Eagle (Aquila chrysaetos), over several regions within the State of California, USA. Primary drivers of flight AGL, those to which eagles showed the same response at all spatial scales, were topographic roughness, ground elevation and the east-west component of aspect (eastness). Each of these is related to formation of thermal updrafts. Secondary drivers, those to which eagles showed region-specific patterns, included topographic position, percent slope, and the north-south component of aspect (northness). In contrast to primary drivers, these secondary drivers were related to formation of both thermal and orographic updrafts. Overall, drivers of flight altitudes that were related to thermal updrafts showed different levels of complexity due to spatial and temporal variation of those drivers than did flight altitudes related to orographic updrafts.

Sign in / Sign up

Export Citation Format

Share Document