scholarly journals Landslide susceptibility on selected slopes in Dzanani, Limpopo Province, South Africa

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Makia L. Diko ◽  
Shallati C. Banyini ◽  
Batobeleng F. Monareng
Keyword(s):  
Landslide Susceptibility ◽  
Critical State ◽  
Anthropogenic Activities ◽  
Limpopo Province ◽  
Silty Clay ◽  
Clay Loam ◽  
Landslide Occurrence ◽  
Landslide Event ◽  
Physical Attributes

Inherent soil properties and anthropogenic activities on slope faces are considered potential recipes for landslide occurrence. The objectives of this study were to physically characterise unconsolidated soils and identify on-going anthropogenic activities on selected slopes in Dzanani in order to appraise their role as contributory factors in enhancing landslide susceptibility. Methods employed for this study comprised mapping, description of soil profile, identification of anthropogenic activities, as well as experimental determination of soil colour, particle size distribution and Atterberg limits. Geologically, the study area comprised rocks of the Fundudzi, Sibasa and Tshifhefhe Formations, ascribed to the Soutpansberg Group. Digging of foundations for construction purposes and subsistence agriculture were identified as the main anthropogenic activities. The soils were predominantly reddish-yellow in colour, texturally variable (silty clay – clayey – silty clay loam and clay loam) and of medium plasticity. Compared to soils from other parts of the world developed on volcanic cones or associated with a landslide event, those from Dzanani were qualified as generally inactive and not prone to landslides. Although the physical attributes suggested the soils were not at a critical state, on-going anthropogenic activities may enhance deep weathering and ultimately alter current soil physical characteristics to a critical state.

Application of Data-Driven Techniques For Landslide Susceptibility Prediction Along An Earthquake Affected Road Section in Kashmir Himalaya

2021 ◽  
Author(s):  
Syed Ahsan Hussain Gardezi ◽  
Nadeem Ahmad Usmani ◽  
Xiao-qing Chen ◽  
Nawaz Ikram ◽  
Sajjad Ahmad ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Anthropogenic Activities ◽  
Data Driven ◽  
Weight Of Evidence ◽  
Support Vector ◽  
Operating Characteristics ◽  
Kashmir Earthquake ◽  
Landslide Occurrence ◽  
Statistical Measures ◽  
Road Section

Abstract The interaction of seismic events with geo-environmental conditions and anthropogenic activities may exacerbate the risk of landslide hazard in a mountainous region. As an example of this, 2005 Kashmir earthquake triggered a large number of shallow to deep slope failures, which was further intensified in following years by human activities notably along road networks, posing a long-term hazard. Hence, this study was planned to evaluate the effectiveness of landslide susceptibility prediction along earthquake affected road-section of Neelum Highway using six different data-driven models. We applied analytical hierarchy process as heuristic approach, weight of evidence and index of entropy as statistical models and multi-layer perceptron, support vector machine and binary logistic regression (BLR) as machine learning models. Initially, 224 landslides locations were marked through field surveys to prepare landslide inventory which was further randomly divided into training (70%) and testing (30%) datasets. Then, 13 landslide causative factors (LCFs) were extracted from geo-spatial database and analysed by measuring collinearity among factors and assessing their contribution in landslide occurrence using different feature selection methods for inclusion in susceptibility modelling. Thereafter, six employed models were trained to produced landslide susceptibility maps of investigated road-section. Finally, the area under receiver operating characteristics (AU-ROC) curve and various statistical measures were applied to validate and compare the performance of modeled landslide susceptibility. The results revealed that no collinearity issue exists among all 13 LCFs, and all six models exhibited satisfying performance in predicting landslide susceptibility of study area. However, BLR model have produced most promising and optimum results as compared to other models with AU-ROC (0.881), Matthew’s correlation coefficient (0.609), Kappa coefficient (0.604), accuracy (0.797) and F-score (0.787). The outcomes of this study can be used as pertinent guide for preventing and managing the landslide disaster risk along Neelum Highway and beyond.

scholarly journals Remote Sensing and GIS Approach for Landslide Susceptibility Mapping: A Case Study in Hofu City, Yamaguchi, Japan

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Benita Nathania ◽  
Fusanori Muira
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
Landslide Event

Landslide is one of the natural hazards that often initiates by the interaction between environmental factors and triggering factor. The identi?cation of areas where landslides are likely to occur is important for the reduction of potential damage. This study utilizes remote sensing data and Geographic Information System (GIS) to identify areas where landslides are likely to occur and generates landslide susceptibility map based on logistic regression model. The study area is located in Hofu city, Yamaguchi prefecture, Japan. The data that were used in this study are satellite imagery from ALOS AVNIR-2, elevation and geology data from GSI, Rainfall data from AMEDAS, and landslide inventory map provided from Ministry of Land, Infrastructure, Transportation and Tourism. The result from this study revealed that elevation from > 50 to < 350 m, slope angle from> 5° to < 50°, slope direction of north and northeast, land cover of agriculture, urban, bare soil, and forest, and lithology of graniodorite, fan deposits, and middle terrace are favorable for landslide occurrence. The landslide susceptiility map showed that 98% of the result calculations of logistic regression are similar to the historical data of landslide event which is among 911 landslide points, 899 points were existed in high and very high susceptibility areas.

scholarly journals Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories

2009 ◽  
Vol 9 (3) ◽  
pp. 673-686 ◽  
Author(s):  
D. B. Kirschbaum ◽  
R. Adler ◽  
Y. Hong ◽  
A. Lerner-Lam
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Hazard ◽  
Soil Conditions ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
Soil Variables ◽  
Topographic Slope ◽  
Predictive Skill ◽  
Landslide Event ◽  
Susceptibility Model

Abstract. Most landslide hazard assessment algorithms in common use are applied to small regions, where high-resolution, in situ, observables are available. A preliminary global landslide hazard algorithm has been developed to estimate areas of potential landslide occurrence in near real-time by combining a calculation of landslide susceptibility with satellite derived rainfall estimates to forecast areas with increased potential for landslide conditions. This paper presents a stochastic methodology to compare this new, landslide hazard algorithm for rainfall-triggered landslides with a newly available inventory of global landslide events, in order to determine the predictive skill and limitations of such a global estimation technique. Additionally, we test the sensitivity of the global algorithm to its input observables, including precipitation, topography, land cover and soil variables. Our analysis indicates that the current algorithm is limited by issues related to both the surface-based susceptibility map and the temporal resolution of rainfall information, but shows skill in determining general geographic and seasonal distributions of landslides. We find that the global susceptibility model has inadequate performance in certain locations, due to improper weighting of surface observables in the susceptibility map. This suggests that the relative contributions of topographic slope and soil conditions to landslide susceptibility must be considered regionally. The current, initial forecast system, although showing some overall skill, must be improved considerably if it is to be used for hazard warning or detailed studies. Surface and remote sensing observations at higher spatial resolution, together with improved landslide event catalogues, are required if global landslide hazard forecasts are to become an operational reality.

Effect The Treatments of Biofertilizer and Mineral Fertilizer on Content of N P K of Soil Cultivated With Crop Corn (Zea Mays L).

2019 ◽  
Vol 9 (2) ◽  
pp. 247-256
Author(s):  
Mohammed Aajmi Salman ◽  
Jawad A. Kamal Al-Shibani
Keyword(s):  
Zea Mays ◽  
Zea Mays L ◽  
Mineral Fertilizer ◽  
Silty Clay ◽  
Clay Loam ◽  
Potassium Fertilizer ◽  
Clay Loam Soil ◽  
Silty Clay Loam ◽  
Fall Season ◽  
Loam Soil

Beneficial microorganisms play a key role in the availability of ions minerals in the soil and use Randomized Complete Block Desing ( R.C.B.D ). The objective of this paper to the study effect of the of biofertilizer and miniral treatments on availability of NPK for crop corn zea mays L.Two types of biofertilizer are Bacterial Bacillus subtilis and Fungal Trichoderma harianum. Three levels of potassium fertilizer are (2.9533, 0.4000 and 2.9533). A field experiment in fall season of 2018 Has been conducted in silty clay loam soil. The experimental Results indicated that Bacillus and Trichoderma inoculation separately or together Have made a significant effect to increase in the availability of N P K in the soil compare to other treatments. The grain yield is where (2.9533, 0.4000 and 2.9533) of bacterial and fungal bio-fertilizer and potassium fertilizers respectively as compared to the control.

scholarly journals A susceptibility-based rainfall threshold approach for landslide occurrence

2019 ◽  
Vol 19 (4) ◽  
pp. 775-789 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin
Keyword(s):  
Landslide Susceptibility ◽  
Rainfall Threshold ◽  
Exceedance Probability ◽  
Equal Weight ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Continental Scale ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

scholarly journals Extension of an extraction method for the determination of 305 organic compounds in clay-loam soil to soils of different characteristics

MethodsX ◽  
2021 ◽  
pp. 101476
Author(s):  
Andrea Acosta-Dacal ◽  
Cristian Rial-Berriel ◽  
Ricardo Díaz-Día ◽  
María del Mar Bernal-Suárez ◽  
Manuel Zumbado ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Extraction Method ◽  
Clay Loam ◽  
Clay Loam Soil ◽  
Loam Soil ◽  
Different Characteristics

scholarly journals Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5906
Author(s):  
Xiaoting Zhou ◽  
Weicheng Wu ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
Renxiang Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Landslide Susceptibility ◽  
Urban Areas ◽  
Support Vector ◽  
Susceptibility Map ◽  
Human Society ◽  
Learning Approaches ◽  
Prevention Measures ◽  
Landslide Occurrence ◽  
Better Than

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

scholarly journals From towers to walls: Trump’s border wall as entrepreneurial performance

2021 ◽  
pp. 239965442110030
Author(s):  
Åshild Kolås ◽  
Lacin ldil Oztig
Keyword(s):  
Control Strategies ◽  
Family Separation ◽  
Deferred Action ◽  
Strong Focus ◽  
Mexico Border ◽  
Physical Attributes ◽  
Southern Border ◽  
Personal Skills ◽  
Presidential Election Campaign

The pledge to build a “great”, “beautiful” southern border wall was a cornerstone of Donald J. Trump’s 2016 presidential election campaign. This paper analyzes Trump’s border wall project as an example of performative statecraft, suggesting that the wall works better rhetorically, than as a barrier against unauthorized cross-border movement. Identifying Trump’s performative statecraft as “entrepreneurial”, we argue that his border wall discourse differs from that of earlier presidents in the way Trump meshes the performance of the border wall as a protective device with his own performance as an entrepreneur and developer. Trump’s border wall discourse accentuates his personal skills as an entrepreneur, and makes these skills relevant to his key campaign promises: to “Make America Great Again”, and defend the nation against transnational crime. Despite Trump’s radical reformulation of US asylum policy, enhanced pursuit of unauthorized immigrants, termination of Obama-era programs like Deferred Action for Childhood Arrivals (DACA), and disturbing but short-lived family separation and ‘Zero-Tolerance’ detention scheme, the border security policies of President Trump are not as novel as his promotional campaigns would have us believe. In fact, Trump’s border control strategies have continued many of the measures introduced by earlier presidents. The novelty of the Trump presidency lies in the strong focus on the new US–Mexico border wall, and fervent attention to the physical attributes and instrumental functions of the wall. Much more than a fence, Trump’s proposed border wall is a grand, awe-inspiring monument to national security, and to Trump’s entrepreneurial statecraft. It also works as a visual aide for Trump’s plan to “Make America Great Again”. Border walls stand as testimony to the power of the state, and the determination of those who defend it. Trump’s border wall would be no exception.

scholarly journals Soil quality of the tea and apple-growing sites in Mindat District, Chin state, Myanmar: A preliminary study

2021 ◽  
Vol 70 (1) ◽  
pp. 27-40
Author(s):  
Yaung Kwee ◽  
Khin Soe
Keyword(s):  
Physicochemical Properties ◽  
Silty Clay ◽  
Subtropical Climate ◽  
Clay Loam ◽  
Silty Clay Loam ◽  
Preliminary Study ◽  
Phosphorus And Potassium ◽  
Very High ◽  
Physicochemical Properties Of Soils

In this study, two sites from tea and apple growing sites were collected from Pyay village and Nine Mile village, Mindat district, Chin state of West Myanmar under a humid subtropical climate. The results of physicochemical properties of observed soils were neutral pH, favorable moisture, silty clay loam texture, very high content of organic carbon, organic matter and total nitrogen. However, the tea growing soil was very poor in phosphorus and potassium content. Moreover, both soils lack of available potassium. The content of heavy metals in both soils was not varied from each other and followed the order: Fe (iron) > Cu (copper) >Zn (zinc) > Pb (lead) > Cr (chromium) and was below the maximum allowed concentrations (MAC). Therefore, the studied soils are generally favorable for cultivation under the condition of application of phosphorus and potassium fertilizers. However, due to the regular application of fertilizers and pesticides, it is necessary to monitor these soils for PTE levels. Further research is recommended, which must include analyses of the physicochemical properties of soils to a two depths of 0-30 and 30-60 cm, especially for the area where fruit plants are grown. In addition, higher density of soil samples and sub-samples are necessary to produce a reliable dataset that will allow proper statistical analysis.

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