landslide occurrence
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2022 ◽  
Author(s):  
S. Modugno ◽  
S. C. M. Johnson ◽  
P. Borrelli ◽  
E. Alam ◽  
N. Bezak ◽  
...  

AbstractDecision-making plays a key role in reducing landslide risk and preventing natural disasters. Land management, recovery of degraded lands, urban planning, and environmental protection in general are fundamental for mitigating landslide hazard and risk. Here, we present a GIS-based multi-scale approach to highlight where and when a country is affected by a high probability of landslide occurrence. In the first step, a landslide human exposure equation is developed considering the landslide susceptibility triggered by rain as hazard, and the population density as exposed factor. The output, from this overview analysis, is a global GIS layer expressing the number of potentially affected people by month, where the monthly rain is used to weight the landslide hazard. As following step, Logistic Regression (LR) analysis was implemented at a national and local level. The Receiver Operating Characteristic indicator is used to understand the goodness of a LR model. The LR models are defined by a dependent variable, presence–absence of landslide points, versus a set of independent environmental variables. The results demonstrate the relevance of a multi-scale approach, at national level the biophysical variables are able to detect landslide hotspot areas, while at sub-regional level geomorphological aspects, like land cover, topographic wetness, and local climatic condition have greater explanatory power.


Landslides ◽  
2022 ◽  
Author(s):  
Meng-Chia Weng ◽  
Cheng-Han Lin ◽  
Wen-Jie Shiu ◽  
Wei-An Chao ◽  
Chia-Chi Chiu ◽  
...  

AbstractMega-earthquakes and extreme climate events accompanied by intrinsic fragile geology lead to numerous landslides along mountain highways in Taiwan, causing enormous life and economic losses. In this study, a system for rapid slope disaster information integration and assessment is proposed with the aim of providing information on landslide occurrence, failure mechanisms, and subsequent landslide-affected areas to the highway authority rapidly. The functionality of the proposed system is deployed into three units: (1) geohazard rapid report (GeoPORT I), (2) multidisciplinary geological survey report (GeoPORT II), and (3) site-specific landslide simulation report (GeoPORT III). After landslide occurrence, the seismology-based monitoring network rapidly provides the initial slope disaster information, including preliminary location, event magnitude, earthquake activity, and source dynamics, within an hour. Within 3 days of the landslide, a multidisciplinary geological survey is conducted to collect high-precision topographical, geological, and remote-sensing data to determine the possible failure mechanism. After integrating the aforementioned information, a full-scale three-dimensional landslide simulation based on the discrete element method is performed within 10 days to reveal the failure process and to identify the areas potentially affected by subsequent disasters through scenario modeling. Overall, the proposed system can promptly provide comprehensive and objective information to relevant authorities after the event occurrence for hazard assessment. The proposed system was validated using a landslide event in the Central Cross-Island Highway of Taiwan.


2022 ◽  
Vol 74 (1) ◽  
Author(s):  
Bing Sheng Wu ◽  
Ray Y. Chuang ◽  
Yi-Chin Chen ◽  
Ya-Shien Lin

AbstractEarthquake-triggered landslides are common disasters of active mountain belts. Due to the lack of earthquake-triggered landslide inventory in Taiwan, it is not intuitive to observe spatial relationships and discover unique patterns between landslides and essential triggers. We examined strong earthquake events in Taiwan after the 1999 Mw7.6 Chi-Chi earthquake and targeted the 2013 ML6.5 Nantou earthquake to create the landslide inventory. We adopted two Landsat-8 satellite images before and after the event to detect landslides, and incorporated a 20-m DEM and rock type data of Taiwan to represent key factors triggering earthquake-induced landslides such as peak ground acceleration (PGA), lithology, slope roughness, slope, and aspect. Based on the analysis of the density of landslides, there are strong correlations between the landslide occurrence and seismic and geomorphic factors. Furthermore, we noticed that the landslide aspects have a systematic tendency towards the northeast, which is not correlated with the dip directions and wave propagation directions. Instead, we found that the northeastward landslide aspect is more associated with the westward–southwestward surface movement at the landslides. We found that the included angles between the landslide aspects and the displacement directions for all the landslides are  ~ 100°–180°. The relationship indicated that the coseismic deformation of the Nantou earthquake may play a role in the landslide distribution. Graphical Abstract


2021 ◽  
Author(s):  
Syed Ahsan Hussain Gardezi ◽  
Nadeem Ahmad Usmani ◽  
Xiao-qing Chen ◽  
Nawaz Ikram ◽  
Sajjad Ahmad ◽  
...  

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.


Geologija ◽  
2021 ◽  
Vol 64 (2) ◽  
pp. 159-171
Author(s):  
Mateja JEMEC AUFLIČ ◽  
Gašper BOKAL ◽  
Špela KUMELJ ◽  
Anže MEDVED ◽  
Mojca DOLINAR ◽  
...  

Slovenia is affected by extreme and intense rainfall that triggers numerous landslides every year, resulting in significant human impact and damage to infrastructure. Previous studies on landslides have shown how rainfall patterns can influence landslide occurrence, while in this paper, we present one of the first study in Slovenia to examine the impact of climate change on landslides in the mid-21st century. To do this, we used the Representative Concentration Pathway (RCP) 4.5 climate scenario and future climatology simulated by six climate models that differed from each other as much as possible while representing measured values of past climate variables as closely as possible. Based on baseline period (1981-2010) we showed the number of days with exceedance of rainfall thresholds and the area where landslides may occur more frequently in the projection period (2041-2070). We found that extreme rainfall events are likely to occur more frequent in the future, which may lead to a higher frequency of landslides in some areas.


2021 ◽  
Author(s):  
Daniel Germain ◽  
Sébastien Roy ◽  
Antonio Jose Teixera Guerra

In the tropical environment such as Brazil, the frequency of rainfall-induced landslides is particularly high because of the rugged terrain, heavy rainfall, increasing urbanization, and the orographic effect of mountain ranges. Since such landslides repeatedly interfere with human activities and infrastructures, improved knowledge related to spatial and temporal prediction of the phenomenon is of interest for risk management. This study is an analysis of empirical rainfall thresholds, which aims to establish local and regional scale correlations between rainfall and the triggering of landslides in Angra dos Reis in the State of Rio de Janeiro. A statistical analysis combining quantile regression and binary logistic regression was performed on 1640 and 526 landslides triggered by daily rainfall over a 6-year period in the municipality and the urban center of Angra dos Reis, in order to establish probabilistic rainfall duration thresholds and assess the role of antecedent rainfall. The results show that the frequency of landslides is highly correlated with rainfall events, and surprisingly the thresholds in dry season are lower than those in wet season. The aspect of the slopes also seems to play an important role as demonstrated by the different thresholds between the southern and northern regions. Finally, the results presented in this study provide new insight into the spatial and temporal dynamics of landslides and rainfall conditions leading to their activation in this tropical and mountainous environment.


Geosciences ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 495
Author(s):  
Hasnaa Harmouzi ◽  
Romy Schlögel ◽  
Marta Jurchescu ◽  
Hans-Balder Havenith

This study presents the results of a landslide susceptibility analysis applied to the Vrancea-Buzău seismogenic region in the Carpathian Mountains, Romania. The target area is affected by a large diversity of landslide processes. Slopes are made-up of various types of rocks, climatic conditions can be classified as wet, and the area is a seismically active one. All this contributes to the observed high landslide hazard. The paper analyses the spatial component of the landslide hazard affecting the target area, the regional landslide susceptibility. First, an existing landslide inventory was completed to cover a wider area for the landslide susceptibility analysis. Second, two types of methods are applied, a purely statistical technique, based on correlations between landslide occurrence and local conditions, as well as the simplified spatial process-based Newmark Displacement analysis. Landslide susceptibility maps have been produced by applying both methods, the second one also allowing us to simulate different scenarios, based on various soil saturation rates and seismic inputs. Furthermore, landslide susceptibility was computed both for the landslide source and runout zones—the first providing information about areas where landslides are preferentially triggered and the second indicating where landslides preferentially move along the slope and accumulate. The analysis showed that any of the different methods applied produces reliable maps of landslide susceptibility. However, uncertainties were also outlined as validation is insufficient, especially in the northern area, where only a few landslides could be mapped due to the intense vegetation cover.


2021 ◽  
Vol 3 ◽  
pp. 1-6
Author(s):  
Dávid Gerzsenyi

Abstract. Locating landslide-prone slopes is important, as landslides often threaten life or property where they occur. There is an abundance of statistical methods in the literature for estimating susceptibility to landslides, i.e., the likelihood of landslide occurrence based on the analyzed conditions. Still, there is a lack of readily available GIS tools for landslide susceptibility analysis, making it hard to reproduce or compare the results of different susceptibility assessments. The FRMOD is a Python-based tool for conducting landslide susceptibility analysis with the frequency ratio method. The frequency ratio method yields susceptibility estimates by comparing the frequency distributions of a set of variables from the sample landslide areas to the distributions for the whole study area. The estimates show the level of similarity to the sample landslides. The two main inputs of the tool are the raster grids of the analyzed continuous (e.g., elevation, slope) and thematic (e.g., lithology) variables and the mask grid that marks the landslide and the non-landslide areas. The analysis is performed with cross-validation to measure the predictive performance of the model. Data computed during the analysis is stored along the final susceptibility estimates and the supplementary statistics. The script reads and writes GDAL-compatible rasters, while the statistics can be saved as text files. Basic plotting functionalities for the grids and the statistics are also built-in to quicken the evaluation of the results. FRMOD enables the swift testing of different analysis setups and to apply the same analysis method for different areas with relative ease.


2021 ◽  
pp. 305-313
Author(s):  
N. H. N. Khalid ◽  
Fathoni Usman ◽  
R. C. Omar ◽  
S. Norhisham

Geology ◽  
2021 ◽  
Author(s):  
Colin K. Bloom ◽  
Andrew Howell ◽  
Timothy Stahl ◽  
Chris Massey ◽  
Corinne Singeisen

Coseismic landslides are observed in higher concentrations around surface-rupturing faults. This observation has been attributed to a combination of stronger ground motions and increased rock mass damage closer to faults. Past work has shown it is difficult to separate the influences of rock mass damage from strong ground motions on landslide occurrence. We measured coseismic off-fault deformation (OFD) zone widths (treating them as a proxy for areas of more intense rock mass damage) using high-resolution, three-dimensional surface displacements from the 2016 Mw 7.8 Kaikōura earthquake in New Zealand. OFD zones vary in width from ~50 m to 1500 m over the ~180 km length of ruptures analyzed. Using landslide densities from a database of 29,557 Kaikōura landslides, we demonstrate that our OFD zone captures a higher density of coseismic landslide incidence than generic “distance to fault rupture” within ~650 m of surface fault ruptures. This result suggests that the effects of rock mass damage within OFD zones (including ground motions from trapped and amplified seismic waves) may contribute to near-fault coseismic landslide occurrence in addition to the influence of regional ground motions, which attenuate with distance from the fault. The OFD zone represents a new path toward understanding, and planning for, the distribution of coseismic landslides around surface fault ruptures. Inclusion of estimates of fault zone width may improve landslide susceptibility models and decrease landslide risk.


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