Factors Contributing to Landslide Susceptibility of the Kope Formation, Cincinnati, Ohio

2021 ◽  
Author(s):  
Michael P. Glassmeyer ◽  
Abdul Shakoor
Keyword(s):  
Shear Strength ◽  
Landslide Susceptibility ◽  
Stream Water ◽  
Strength Parameters ◽  
Slake Durability Index ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Colluvial Soil ◽  
Kope Formation ◽  
Representative Samples

ABSTRACT The objective of this study was to evaluate the factors that contribute to the high frequency of landslides in the Kope Formation and the overlying colluvial soil present in the Cincinnati area, southwestern Ohio. The Kope Formation consists of approximately 80 percent shale inter-bedded with 20 percent limestone. The colluvium that forms from the weathering of the shale bedrock consists of a low-plasticity clay. Based on field observations, LiDAR data, and information gathered from city and county agencies, we created a landslide inventory map for the Cincinnati area, identifying 842 landslides. From the inventory map, we selected 10 landslides that included seven rotational and three translational slides for detailed investigations. Representative samples were collected from the landslide sites for determining natural water content, Atterberg limits, grain size distribution, shear strength parameters, and slake durability index. For the translational landslides, strength parameters were determined along the contact between the bedrock and the overlying colluvium. The results of the study indicate that multiple factors contribute to landslide susceptibility of the Kope Formation and the overlying colluvium, including low shear strength of the colluvial soil, development of porewater pressure within the slope, human activity such as loading the top or cutting the toe of a slope, low to very low durability of the bedrock that allows rapid disintegration of the bedrock and accumulation of colluvial soil, undercutting of the slope toe by stream water, and steepness of the slopes.

scholarly journals An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

2020 ◽  
Vol 9 (10) ◽  
pp. 561
Author(s):  
Omid Ghorbanzadeh ◽  
Khalil Didehban ◽  
Hamid Rasouli ◽  
Khalil Valizadeh Kamran ◽  
Bakhtiar Feizizadeh ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Data Driven ◽  
Susceptibility Maps ◽  
Normalised Difference Vegetation Index ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Sentinel 2

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

scholarly journals Earthquake-Induced Shallow Landslide Susceptibility Assessment of Gorkha District

2019 ◽  
Vol 5 ◽  
pp. 181-193
Author(s):  
Tapendra Kumar Shahi
Keyword(s):  
Landslide Susceptibility ◽  
Large Scale ◽  
Shallow Landslide ◽  
Earthquake Ground Motion ◽  
Susceptibility Map ◽  
Topographic Effects ◽  
Gorkha Earthquake ◽  
Loss Of Life ◽  
Landslide Inventory Map ◽  
Inventory Map

Nepal is very seriously affected by landslides every year causing loss of life and property. Large scale earthquakes that occurred in different time periods such as on 15th January, 1934 or that on 25th April 2015 have proved Nepal as seismically vulnerable -place. Nepal has witnessed several landslides during and after the earthquake events making some areas of land quite vulnerable for settlement and other usages. Therefore in order to minimize the impacts of landslides caused due to earthquakes, highly susceptible locations should be identified and spatial planning is made accordingly. Considering topographic effects in amplification of earthquake ground motion, Uchida et al. (2004) have developed a topographical parameter based empirical description of landslide susceptibility during an earthquake. In this research, the method proposed by Uchida et al. (2004) is utilized in raster GIS and landslide susceptibility analysis is performed in the study area of SulikotGaupalika of Gorkha district, Nepal which was severely hit by several landslides due to “Gorkha Earthquake 2015". The landslide inventory map of SulikotGaupalika due to “Gorkha Earthquake 2015" is obtained and is correlated with landslide susceptibility values as obtained by using Uchida et al. (2004). The analysis shows that the method proposed by Uchida et al. (2004) is more than 68.9% accurate in delineating the probable locations of earthquake induced landslides. By calibrating landslide data and landslide susceptibility values in a small site (i.e. SulikotGaupalika) within the study area, a final landslide susceptibility map is prepared for the whole study area of Gorkha district. The resultant susceptibility map is very useful for planning settlements, development activities and reconstruction planning.

scholarly journals Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks

2005 ◽  
Vol 5 (6) ◽  
pp. 979-992 ◽  
Author(s):  
M. Ercanoglu
Keyword(s):  
Black Sea ◽  
Landslide Susceptibility ◽  
Landslide Inventory ◽  
The West ◽  
Data Set ◽  
Black Sea Region ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
West Black Sea ◽  
West Black Sea Region

Abstract. Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN) method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index) were used. To obtain maps of these parameters, Digital Elevation Model (DEM) and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi). Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (rij) value. Much of the areal extents of the landslides (87.2%) were classified as susceptible to landsliding, and rij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.

scholarly journals Evaluation of Landslide Susceptibility of Şavşat District of Artvin Province (Turkey) Using Machine Learning Techniques

2021 ◽  
Author(s):  
Halil Akinci ◽  
Mustafa Zeybek ◽  
Sedat Dogan
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Susceptibility Map ◽  
Success Rates ◽  
Slope Length ◽  
Prediction Rate ◽  
Landslide Inventory Map ◽  
Inventory Map

The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Province using machine learning (ML) models and to compare the predictive performances of the models used. Tree-based ensemble learning models, including random forest (RF), gradient boosting machines (GBM), and extreme gradient boosting (XGBoost), were used in the study. A landslide inventory map consisting of 85 landslide polygons was used in the study. The inventory map comprises 32,777 landslide pixels at 30 m resolution. Randomly selected 70% of the landslide pixels were used for training the models and the remaining 30% were used for the validation of the models. In susceptibility analysis, altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover, lithology, slope, slope length, and topographic wetness index parameters were used. The validation of the models was conducted using success and prediction rate curves. The validation results showed that the success rates for the GBM, RF, and XGBoost models were 91.6%, 98.4%, and 98.6%, respectively, whereas the prediction rate were 91.4%, 97.9%, and 98.1%, respectively. Therefore, it was concluded that landslide susceptibility map produced with XGBoost model can help decision makers in reducing landslide-associated damages in the study area.

scholarly journals Evaluation of Undrained Shear Strength of Miocene Clay from the Weight Sounding Test (WST)

2017 ◽  
Vol 62 (2) ◽  
pp. 367-384
Author(s):  
Sebastian Olesiak
Keyword(s):  
Shear Strength ◽  
Field Testing ◽  
Field Investigation ◽  
Undrained Shear Strength ◽  
Cohesive Soils ◽  
Strength Parameters ◽  
Additional Measurement ◽  
Carpathian Foredeep ◽  
Innovative Solution ◽  
Undrained Shear

Abstract Soil strength parameters needed for the calculation of bearing capacity and stability are increasingly determined from field testing. This paper presents a method to determine the undrained shear strength cuWST of the soil, based on the Weight Sounding Test (WST). The innovative solution which allows for a significant reduction of equipment needed for geotechnical field investigation is presented. The proposed method is based on an additional measurement of the torque during testing. It then becomes possible to estimate the undrained shear strength, cuWST of the soil, using the correlation given in this paper. The research results presented in this paper were carried out on selected cohesive soils, Miocene clays from the Carpathian Foredeep.

scholarly journals Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

Landslides ◽  
2021 ◽  
Author(s):  
Sansar Raj Meena ◽  
Omid Ghorbanzadeh ◽  
Cees J. van Westen ◽  
Thimmaiah Gudiyangada Nachappa ◽  
Thomas Blaschke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Western Ghats ◽  
Auxiliary Information ◽  
Optical Data ◽  
Spectral Information ◽  
Topographic Slope ◽  
Western Ghats Of India ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
The Western Ghats

AbstractRainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions.

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