A statistical model for presence of late-season frozen ground in discontinuous permafrost at Dublin Gulch, Yukon

2013 ◽  
Vol 50 (8) ◽  
pp. 889-898
Author(s):  
P.E. Quinn
Keyword(s):  
Statistical Model ◽  
Soil Type ◽  
Slope Angle ◽  
Slope Aspect ◽  
Terrain Analysis ◽  
Borehole Data ◽  
Frozen Ground ◽  
Late Season ◽  
Topographic Data ◽  
Discontinuous Permafrost

This paper describes geostatistical analyses completed at a discontinuous permafrost site in central Yukon to develop a predictive model for the presence of late-season frozen ground in support of planning and design for potential site development. The most important factors in the bivariate statistical model were soil type, as determined through terrain analysis, and slope aspect, as inferred from available topographic data. The other three factors included in the final model were profile curvature, slope angle, and ground elevation, each interpreted from available topographic data. The resulting model subdivides the site into three broad classes of frozen ground likelihood: low, where frozen ground can be expected to be encountered in late summer at 15% of observation locations; medium, where 50% of the ground is expected to remain frozen; and high, where 85% of the ground is expected to remain frozen. New test pit and borehole data from the summer of 2012 were used to verify model performance. The inferred correlations between frozen ground and soil type, aspect, curvature, slope, and elevation obtained in this case study may provide useful information relative to expected permafrost occurrence at sites in central Yukon with similar geology and physiography.

scholarly journals How Can Geomorphology Inform Ecological Restoration? A Synthesis of Geophysical and Biological Assessment to Determine Restoration Priorities

2021 ◽  
Author(s):  
◽  
Leicester Cooper
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Reference Site ◽  
Slope Angle ◽  
Slope Aspect ◽  
Total N ◽  
Olsen P ◽  
Restoration Site ◽  
Soil Variables ◽  
Indigenous Forest

<p>The central concern that this study addresses is how an understanding of geomorphological processes and forms may inform ecological restoration; particularly practical restoration prioritisation. The setting is that of a hill country gully system covered in grazing pasture which historically would have been cloaked in indigenous forest. The study examines theory in conjunction with an application using a case study centred on Whareroa Farm (the restoration site) and Paraparaumu Scenic Reserve (the reference site) on the southern Kapiti Coast, north of Wellington. The impact that the change of land use has had on the soil and geomorphic condition of Whareroa and the influence the changes may have on the sites restoration is investigated. The thesis demonstrates a method of choosing reference sites to be used as templates for rehabilitating the restoration site. Geographical Information Systems and national databases are used and supplemented with site inspection. The reference site chosen, Paraparaumu Scenic Reserve, proved to be a good template for the restoration site particularly given that it is located in the midst of a heavily modified area. On-site inspection considering dendritic pattern and floristic composition confirms the database analysis results. Soil variables (bulk density, porosity, soil texture, pH, Olsen P, Anaerobic Mineralisable N, Total N (AMN), Total C and C:N ratio) are investigated and statistical comparisons made between the sites to quantify changes due to land-use change, i.e. deforestation and subsequent pastoral grazing. Factors investigated that may explain the variation in the soil variables were site (land use), hillslope location, slope aspect, and slope angle. Permutation tests were conducted to investigate the relationships between the independent factors and the SQI (dependent soil variables). Land use and slope angle were most frequent significant explanatory factors of variation, followed by hillslope location whilst slope aspect only influenced soil texture. A number of soil variables at Whareroa were found to be outside the expected range of values for an indigenous forest soil including AMN, Total N, Olsen P, and pH ...</p>

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

Landslide susceptibility mapping using GIS and Remote Sensing: A case study of the Rangamati District, Bangladesh

2020 ◽  
Author(s):  
Afruja Begum ◽  
Md Shofiqul Islam ◽  
Md. Muyeed Hasan
Keyword(s):  
Remote Sensing ◽  
Water Pressure ◽  
Research Work ◽  
Slope Angle ◽  
Steep Slope ◽  
Landslide Susceptibility Mapping ◽  
Dominant Factor ◽  
Natural Phenomenon ◽  
Slope Aspect ◽  
Gis And Remote Sensing

Abstract The landslide is a natural phenomenon and one of the most commonplace disasters in the Rangamati Hill tract area which appeals for better forecasting and specify the landslide susceptible zonation. This research work examines the application of GIS and Remote Sensing techniques based on different parameters such as altitude, slope angle, slope aspect, rainfall, land-use land-cover (LULC), geology and stream distance by heuristic model to identify the landslide susceptible zones for the study area. Among the parameters, rainfall, steep slope, geology and LULC are the dominant factor that triggering the landslide. Clayey or silty soils of the study area during heavy and prolong rainfall behave a flow of debris due to water pressure within the soil, resulting landslides. Steep slope has greater influences for weather zones of the rock-masses for susceptible landslides. Result and field observation indicate that the population density and LULC has a vital effect on landslide within the study area. However, landslide susceptible zones were created based on the susceptibility map of the study area which shows that about 19.43% of the area are at low susceptible zone, 56.55% of the area are at medium susceptible zone, 19.19% of the area are in the high susceptible zone and 4.81% of the area is at the very high susceptible zone.

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

scholarly journals Comparison of Statistical Analysis Models for Susceptibility Assessment of Earthquake-Triggered Landslides: A Case Study from 2015 Earthquake in Lefkada Island

Geosciences ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 350 ◽  
Author(s):  
Christos Polykretis ◽  
Kleomenis Kalogeropoulos ◽  
Panagiotis Andreopoulos ◽  
Antigoni Faka ◽  
Andreas Tsatsaris ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Slope Angle ◽  
Slope Aspect ◽  
Ground Acceleration ◽  
Bivariate Model ◽  
Susceptibility Maps ◽  
Emergency Actions ◽  
Data Layers ◽  
Lefkada Island ◽  
Analysis Models

The main purpose of this study is to comparatively assess the susceptibility of earthquake-triggered landslides in the island of Lefkada (Ionian Islands, Greece) using two different statistical analysis models, a bivariate model represented by frequency ratio (FR), and a multivariate model represented by logistic regression (LR). For the implementation of the models, the relationship between geo-environmental factors contributing to landslides and documented events related to the 17th November 2015 earthquake was investigated by geographic information systems (GIS)-based analysis. A landslide inventory with events attributed to the specific earthquake was prepared using satellite imagery interpretation and field surveys. Eight factors: Elevation, slope angle, slope aspect, distance to main road network, distance to faults, land cover, geology, and peak ground acceleration (PGA), were considered and used as thematic data layers. The prediction capability of the models and the accuracy of the resulting susceptibility maps were tested by a standard validation method, the receiver operator characteristic (ROC) analysis. Based on the validation results, the output map with the highest reliability could potentially constitute an ideal basis for use within regional spatial planning as well as for the organization of emergency actions by local authorities.

scholarly journals Rockfall Investigation and Hazard Assessment from Nang County to Jiacha County in Tibet

2019 ◽  
Vol 10 (1) ◽  
pp. 247 ◽  
Author(s):  
Haiyang Liu ◽  
Xueliang Wang ◽  
Xiaohui Liao ◽  
Juanjuan Sun ◽  
Su Zhang
Keyword(s):  
Rock Mass ◽  
Hazard Assessment ◽  
Cloud Model ◽  
Rapid Development ◽  
Slope Angle ◽  
Project Planning ◽  
Aerial Images ◽  
Slope Aspect ◽  
Human Engineering ◽  
Rockfall Hazard

The influences of rockfall on human engineering have been increasing in Tibet with the rapid development of the western region of China. This study proposed a multi-approach to carry out rockfall investigation and hazard assessment. As a case study, the rockfall hazard from Nang County to Jiacha County in Tibet was assessed. Firstly, we summarized the characteristics of spatial distributions of typical rockfall sources using Digital Elevation Model (DEM) and unmanned aerial vehicle (UAV) aerial images with resolution of 10 m. According to the thresholds of slope angle, slope aspect and elevation distribution of typical rockfall sources, we obtained all of the rockfall source areas in study area semi-automatically in ArcGIS platform. Secondly, we improved the efficiency and accuracy of detailed field investigation by using a three-dimensional (3D) point cloud model and rock mass structure extraction software. According to the analysis result, the dominant joint set was J1, whose orientation was basically consistent with the Yarlung Tsangpo Fault. The combination of J1, J2 and J4 cut the rock mass into blocks of wedge with J1 as potential sliding planes. It was indicated that the stability of the rock mass in study area was mainly controlled by the characters of joint sets. Finally, we applied the improved reclassification criteria of the Rockfall Hazard Vector (RHV) method in rockfall hazard assessment according to protection capabilities of the current protection facilities, making the result more valuable for geohazards prevention work. Based on this multi-approach, we obtained that 10.92% of the 306 provincial highway and 9.38% of the power line were threatened by potential rockfall hazards in study area. The hazard assessment results of study area were also of certain guiding value to the linear project planning and geohazards prevention work.

Zonation of the Landslide Hazards Based on Artificial Neural Network

2012 ◽  
Vol 204-208 ◽  
pp. 3389-3392
Author(s):  
Zhi Wang Wang ◽  
Duan You Li ◽  
Jing Ning
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Result ◽  
Slope Angle ◽  
Slope Aspect ◽  
Gis Technology ◽  
Rs And Gis ◽  
Landslide Hazards ◽  
Artificial Neural ◽  
Elevation Model

This paper applies RS and GIS technology to study zonation of the landslide hazards in the study area from Badong county to Zigui county in TGP reservoir region. The causative factors involves lithology, distance to faults, slope angle, slope aspect, elevation, drainage network, distance to river and distribution of plant, which are derived from geological map, Spot imagery data and Digital Elevation Model (DEM) based on RS and GIS technology. We analyze the zonation of the landslide hazards with artificial neural network. The research result is very coincident with the occurrence of the known landslides in the study area.

scholarly journals Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression

2013 ◽  
Vol 13 (12) ◽  
pp. 3339-3355 ◽  
Author(s):  
M. C. Mărgărint ◽  
A. Grozavu ◽  
C. V. Patriche
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Spatial Variability ◽  
Landslide Susceptibility ◽  
Scientific Literature ◽  
Characteristic Curve ◽  
Slope Angle ◽  
Slope Aspect ◽  
Hazard Mapping ◽  
Correct Identification

Abstract. In landslide susceptibility assessment, an important issue is the correct identification of significant contributing factors, which leads to the improvement of predictions regarding this type of geomorphologic processes. In the scientific literature, different weightings are assigned to these factors, but contain large variations. This study aims to identify the spatial variability and range of variation for the coefficients of landslide predictors in different geographical conditions. Four sectors of 15 km × 15 km (225 km2) were selected for analysis from representative regions in Romania in terms of spatial extent of landslides, situated both on the hilly areas (the Transylvanian Plateau and Moldavian Plateau) and lower mountain region (Subcarpathians). The following factors were taken into consideration: elevation, slope angle, slope height, terrain curvature (mean, plan and profile), distance from drainage network, slope aspect, land use, and lithology. For each sector, landslide inventory, digital elevation model and thematic layers of the mentioned predictors were achieved and integrated in a georeferenced environment. The logistic regression was applied separately for the four study sectors as the statistical method for assessing terrain landsliding susceptibility. Maps of landslide susceptibility were produced, the values of which were classified by using the natural breaks method (Jenks). The accuracy of the logistic regression outcomes was evaluated using the ROC (receiver operating characteristic) curve and AUC (area under the curve) parameter, which show values between 0.852 and 0.922 for training samples, and between 0.851 and 0.940 for validation samples. The values of coefficients are generally confined within the limits specified by the scientific literature. In each sector, landslide susceptibility is essentially related to some specific predictors, such as the slope angle, land use, slope height, and lithology. The study points out that the coefficients assigned to the landslide predictors through logistic regression are capable to reveal some important characteristics in landslide manifestation. The study also shows that the logistic regression could be an alternative method to the current Romanian methodology for landslide susceptibility and hazard mapping.

scholarly journals Landslide Susceptibility Mapping using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

2021 ◽  
Author(s):  
Md. Sharafat Chowdhury ◽  
Bibi Hafsa
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Information Value ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Conditioning Factors

Abstract This study attempts to produce Landslide Susceptibility Map for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). A secondary landslide inventory database was used to correlate the previous landslides with the landslide conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.

scholarly journals A new approach in the development and analysis of the landslide susceptibility map of the hillslopes of Bujumbura, Burundi

2021 ◽  
pp. 26-34
Author(s):  
Desire Kubwimana ◽  
Lahsen Ait Brahim ◽  
Abdellah Abdelouafi
Keyword(s):  
Landslide Susceptibility ◽  
Slope Angle ◽  
Landslide Inventory ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Aerial Photos ◽  
Conditioning Factors ◽  
Mountainous Regions ◽  
Tropical Environments

As in other hilly and mountainous regions of the world, the hillslopes of Bujumbura are prone to landslides. In this area, landslides impact human lives and infrastructures. Despite the high landslide-induced damages, slope instabilities are less investigated. The aim of this research is to assess the landslide susceptibility using a probabilistic/statistical data modeling approach for predicting the initiation of future landslides. A spatial landslide inventory with their physical characteristics through interpretation of high-resolution optic imageries/aerial photos and intensive fieldwork are carried out. Base on in-depth field knowledge and green literature, let’s select potential landslide conditioning factors. A landslide inventory map with 568 landslides is produced. Out of the total of 568 landslide sites, 50 % of the data taken before the 2000s is used for training and the remaining 50 % (post-2000 events) were used for validation purposes. A landslide susceptibility map with an efficiency of 76 % to predict future slope failures is generated. The main landslides controlling factors in ascendant order are the density of drainage networks, the land use/cover, the lithology, the fault density, the slope angle, the curvature, the elevation, and the slope aspect. The causes of landslides support former regional studies which state that in the region, landslides are related to the geology with the high rapid weathering process in tropical environments, topography, and geodynamics. The susceptibility map will be a powerful decision-making tool for drawing up appropriate development plans in the hillslopes of Bujumbura with high demographic exposure. Such an approach will make it possible to mitigate the socio-economic impacts due to these land instabilities

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