scholarly journals Landslide Susceptibility Modelling for Agricultural activities in Hilly Areas

2019 ◽  
Vol 8 (4) ◽  
pp. 6206-6212
Keyword(s):  
Landslide Susceptibility ◽  
Slope Failure ◽  
Regional Scale ◽  
Susceptibility Map ◽  
Detailed Knowledge ◽  
Susceptibility Maps ◽  
Susceptibility Factors ◽  
Cameron Highlands ◽  
Probable Failure ◽  
Made In

The slope failure risk assessment of a particular area can be prepared by considering the data available. Many attempts have been made to classify the risk where evaluations are made in rating or in grading the slopes based on their characteristics and erosion problems. The assessments were done for geo-hazard such as erosion and landslide recognized in planning and guidance. Most of the hazard risk analyses require detailed knowledge of the geo- environmental predisposition factors and initial events that led to failure. The results of these analyses consist of identification and mapping of all erosion induced landslide phenomenon and are often translated in the form of maps, which is the fundamental step of the hazard assessment. The ranking of susceptibility areas and the delineation of probable failure areas are among essential features relevant to the production of these maps. In this study, Landslide Susceptibility Modelling was developed by taking into consideration all the landslide susceptibility factors in Cameron Highlands. The landslide susceptibility map was produced based on the historical records of a landslide in that area for 20 years and the frequency ratio model was developed using mapoverlaying techniques. The susceptibility map offers substantial benefits as a regional-scale tool over earlier susceptibility maps and Cameron Highland landslide- susceptible terrain zoning. The susceptibility map has the advantage of assisting with the implementation of suitable efforts to prevent landslides.

Integrating landslide susceptibility maps into warning models at regional scale in Italy

2021 ◽  
Author(s):  
Gaetano Pecoraro ◽  
Michele Calvello
Keyword(s):  
Spatial Resolution ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Regional Scale ◽  
Susceptibility Map ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

<p>The importance of susceptibility maps in the initial phase of landslide hazard and risk assessment is widely recognized in the literature, since they provide to stakeholders a general overview of the location of landslide prone areas. Usually, the use of these maps is limited to support land use planning. However, many researchers have recently recognized that susceptibility maps may also be used to improve the performance and spatial resolution of landslide warning at regional scale and provide a better updating of hazard assessment over time. Indeed, landslides prediction may be difficult at regional scale only considering rainfall condition, due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geology, and lithology). As a result, a critical issue of models solely based on rainfall thresholds may be the issuing of warnings in areas that are not prone to landslide occurrence, resulting in an excessive number of false positives. In this work, we propose a methodology aimed at combining a susceptibility map and a set of rainfall thresholds by using a matrix approach to refine the performance of an early warning model at regional scale. The main aim is the combination of rainfall thresholds (typically used to accomplish a dynamic temporal forecasting with good temporal resolution but very coarse spatial resolution), with landslide susceptibility maps (providing static spatial information about the probability of landslide occurrence with a finer resolution). The methodology presented herein could allow a better prediction of “where” and “when” landslides may occur, thus: i) allowing to define a time-dependent level of hazard associated to their possible occurrence, and ii) markedly refining the spatial resolution of warning models employed at regional scale, given that areas susceptible to landslides typically represent only a fraction of territorial warning zones.</p>

scholarly journals Strategies investigation in using artificial neural network for landslide susceptibility mapping: application to a Sicilian catchment

2013 ◽  
Vol 16 (2) ◽  
pp. 502-515 ◽  
Author(s):  
Elisa Arnone ◽  
Antonio Francipane ◽  
Leonardo V. Noto ◽  
Antonino Scarbaci ◽  
Goffredo La Loggia
Keyword(s):  
Landslide Susceptibility ◽  
Slope Failure ◽  
Hazard Analysis ◽  
Physical Phenomenon ◽  
Sample Selection ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Maps ◽  
Definition Of ◽  
Basin Area ◽  
Small Basin

Susceptibility assessment of areas prone to landsliding remains one of the most useful approaches in landslide hazard analysis. The key point of such analysis is the correlation between the physical phenomenon and its triggering factors based on past observations. Many methods have been developed in the scientific literature to capture and model this correlation, usually within a geographic information system (GIS) framework. Among these, the use of neural networks, in particular the multi-layer perceptron (MLP) networks, has provided successful results. A successful application of the MLP method to a basin area requires the definition of different model strategies, such as the sample selection for the training phase or the design of the network structure. The present study investigates the effects of these strategies on the development of landslide susceptibility maps by applying different model configurations to a small basin located in northeastern Sicily (Italy), where a number of historical slope failure events have been documented over the years. Model performances and their comparison are evaluated using specific metrics.

scholarly journals Retrospective evaluation of landslide susceptibility maps and review of validation practice

2021 ◽  
Vol 80 (15) ◽  
Author(s):  
Paul Fleuchaus ◽  
Philipp Blum ◽  
Martina Wilde ◽  
Birgit Terhorst ◽  
Christoph Butscher
Keyword(s):  
Landslide Susceptibility ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Susceptibility Map ◽  
Retrospective Evaluation ◽  
Widespread Application ◽  
Evaluation Approach ◽  
Susceptibility Maps ◽  
Landslide Event ◽  
Landslide Susceptibility Maps

AbstractDespite the widespread application of landslide susceptibility analyses, there is hardly any information about whether or not the occurrence of recent landslide events was correctly predicted by the relevant susceptibility maps. Hence, the objective of this study is to evaluate four landslide susceptibility maps retrospectively in a landslide-prone area of the Swabian Alb (Germany). The predictive performance of each susceptibility map is evaluated based on a landslide event triggered by heavy rainfalls in the year 2013. The retrospective evaluation revealed significant variations in the predictive accuracy of the analyzed studies. Both completely erroneous as well as very precise predictions were observed. These differences are less attributed to the applied statistical method and more to the quality and comprehensiveness of the used input data. Furthermore, a literature review of 50 peer-reviewed articles showed that most landslide susceptibility analyses achieve very high validation scores. 73% of the analyzed studies achieved an area under curve (AUC) value of at least 80%. These high validation scores, however, do not reflect the high uncertainty in statistical susceptibility analysis. Thus, the quality assessment of landslide susceptibility maps should not only comprise an index-based, quantitative validation, but also an additional qualitative plausibility check considering local geomorphological characteristics and local landslide mechanisms. Finally, the proposed retrospective evaluation approach cannot only help to assess the quality of susceptibility maps and demonstrate the reliability of such statistical methods, but also identify issues that will enable the susceptibility maps to be improved in the future.

scholarly journals Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake

2019 ◽  
Vol 11 (8) ◽  
pp. 978 ◽  
Author(s):  
Xiaoyi Shao ◽  
Siyuan Ma ◽  
Chong Xu ◽  
Pengfei Zhang ◽  
Boyu Wen ◽  
...  
Keyword(s):  
Success Rate ◽  
Landslide Susceptibility ◽  
Slope Failure ◽  
Slope Angle ◽  
Training Dataset ◽  
Support Vector ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Test Dataset ◽  
Prediction Rate

The 5 September 2018 (UTC time) Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing widespread concern. We attempted to establish a detailed inventory of this slope failure and use proper methods to assess landslide susceptibility in the entire affected area. To this end we applied the logistic regression (LR) and the support vector machine (SVM) for this study. Based on high-resolution (3 m) optical satellite images (planet image) before and after the earthquake, we delineated 9295 individual landslides triggered by the earthquake, occupying an area of 30.96 km2. Ten controlling factors were selected for susceptibility analysis, including elevation, slope angle, aspect, curvature, distances to faults, distances to the epicenter, Peak ground acceleration (PGA), distance to rivers, distances to roads and lithology. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that in the LR model, the success rate is 84.7% between the landslide susceptibility map and the training dataset, and the prediction rate is 83.9% shown by comparing the test dataset and the landslide susceptibility map. In the SVM model, a success rate of 90.9% exists between the susceptibility map and the test samples, and a prediction rate of 87.1% from comparison of the test dataset and the landslides susceptibility map. In comparison, the performance of the SVM is slightly better than the LR model.

GIS-Based Logistic Regression for Landslide Susceptibility Analysis in Western Washington State

2017 ◽  
Vol 8 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Lucas A. Dailey ◽  
Sven Fuhrmann
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Puget Sound ◽  
Washington State ◽  
Susceptibility Map ◽  
The North ◽  
Susceptibility Maps ◽  
Western Washington ◽  
Landslide Susceptibility Maps ◽  
Puget Sound Region

The Oso landslide, one of the most recent disasters, occurred on March 22nd, 2014 in western Washington State. It caused significant property damage and killed over 40 people. As a result, a renewed interest has emerged for creating more accurate landslide susceptibility maps for this region. Research addressing landslide susceptibility within the north Puget Sound region of western Washington is lacking; therefore, this study develops a probabilistic GIS-based landslide susceptibility model for the north Puget Sound region. Multivariate logistic regression was utilized to create a landslide susceptibility map of Whatcom, Skagit, Snohomish, and King Counties. To predict probable areas of landslide occurrence, a landslide inventory map was prepared and fourteen topographic, geologic, environmental, and climatic predictor variables were considered. This research aims to assist in restructuring western Washington's landslide policies, and could serve as the first step in producing more accurate landslide susceptibility maps for the region.

scholarly journals Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

2017 ◽  
Vol 17 (7) ◽  
pp. 1091-1109 ◽  
Author(s):  
Sérgio C. Oliveira ◽  
José L. Zêzere ◽  
Sara Lajas ◽  
Raquel Melo
Keyword(s):  
Landslide Susceptibility ◽  
Test Site ◽  
Information Value ◽  
Susceptibility Map ◽  
Predictive Capacity ◽  
Infinite Slope ◽  
Susceptibility Maps ◽  
Basin Scale ◽  
Deterministic Methods ◽  
Physically Based

Abstract. Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km2) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

scholarly journals Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 148
Author(s):  
Nikolaos Tavoularis ◽  
George Papathanassiou ◽  
Athanassios Ganas ◽  
Panagiotis Argyrakis
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Interaction Matrix ◽  
Susceptibility Map ◽  
Engineering System ◽  
Slope Failures ◽  
Landslide Susceptibility Map ◽  
Rock Engineering ◽  
Entire Area ◽  
Rock Engineering System

The triggering of slope failures can cause a significant impact on human settlements and infrastructure in cities, coasts, islands and mountains. Therefore, a reliable evaluation of the landslide hazard would help mitigate the effects of such landslides and decrease the relevant risk. The goal of this paper is to develop, for the first time on a regional scale (1:100,000), a landslide susceptibility map for the entire area of the Attica region in Greece. In order to achieve this, a database of slope failures triggered in the Attica Region from 1961 to 2020 was developed and a semi-quantitative heuristic methodology called Rock Engineering System (RES) was applied through an interaction matrix, where ten parameters, selected as controlling factors for the landslide occurrence, were statistically correlated with the spatial distribution of slope failures. The generated model was validated by using historical landslide data, field-verified slope failures and a methodology developed by the Oregon Department of Geology and Mineral Industries, showing a satisfactory correlation between the expected and existing landslide susceptibility level. Having compiled the landslide susceptibility map, studies focusing on landslide risk assessment can be realized in the Attica Region.

scholarly journals Regional-Scale Landslide Susceptibility Mapping Using Limited LiDAR-Based Landslide Inventories for Sisak-Moslavina County, Croatia

2021 ◽  
Vol 13 (8) ◽  
pp. 4543
Author(s):  
Iris Bostjančić ◽  
Marina Filipović ◽  
Vlatko Gulam ◽  
Davor Pollak
Keyword(s):  
Predictive Factors ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Spatial Relationship ◽  
Cost Benefit ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
Cost Benefit Ratio ◽  
The Cost

In this paper, for the first time, a regional-scale 1:100,000 landslide-susceptibility map (LSM) is presented for Sisak-Moslavina County in Croatia. The spatial relationship between landslide occurrence and landslide predictive factors (engineering geological units, relief, roughness, and distance to streams) is assessed using the integration of a statistically based frequency ratio (FR) into the analytical hierarchy process (AHP). Due to the lack of landslide inventory for the county, LiDAR-based inventories are completed for an area of 132 km2. From 1238 landslides, 549 are chosen to calculate the LSM and 689 for its verification. Additionally, landslides digitized from available geological maps and reported via the web portal “Report a landslide” are used for verification. The county is classified into four susceptibility classes, covering 36% with very-high and high and 64% with moderate and low susceptibility zones. The presented approach, using limited LiDAR data and the extrapolation of the correlation results to the entire county, is encouraging for primary regional-level studies, justifying the cost-benefit ratio. Still, the positioning of LiDAR polygons prerequires a basic statistical analysis of predictive factors.

scholarly journals Assessing the quality of landslide susceptibility maps – case study Lower Austria

2013 ◽  
Vol 1 (2) ◽  
pp. 1001-1050 ◽  
Author(s):  
H. Petschko ◽  
A. Brenning ◽  
R. Bell ◽  
J. Goetz ◽  
T. Glade
Keyword(s):  
Confidence Intervals ◽  
Landslide Susceptibility ◽  
Spatial Planning ◽  
Cross Validation ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
High Interest ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Landslide susceptibility maps are helpful tools to identify areas which might be prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, the quality of the landslide susceptibility map and of the model applied to compute them is of high interest. In this study we focus on the analysis of the model performance by a repeated k-fold cross-validation with spatial and random subsampling. Furthermore, the focus is on the analysis of the implications of uncertainties expressed by confidence intervals of model predictions. The cross-validation performance assessments reflects the variability of performance estimates compared to single hold-out validation approaches that produce only a single estimate. The analysis of the confidence intervals shows that in 85% of the study area, the 95% confidence limits fall within the same susceptibility class. However, there are cases where confidence intervals overlap with all classes from the lowest to the highest class of susceptibility to landsliding. Locations whose confidence intervals intersect with more than one susceptibility class are of high interest because this uncertainty may affect spatial planning processes that are based on the susceptibility level.

scholarly journals Producing landslide susceptibility maps by applying expert knowledge in a GIS - based environment.

2016 ◽  
Vol 47 (3) ◽  
pp. 1539 ◽  
Author(s):  
P. Tsangaratos ◽  
D. Rozos
Keyword(s):  
Landslide Susceptibility ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Visual Basic ◽  
Susceptibility Map ◽  
Engineering Systems ◽  
Landslide Susceptibility Map ◽  
Related Factors ◽  
Susceptibility Maps ◽  
Analytic Hierarchical Process

In this paper two semi - quantative approaches, from the domain of Multi criteria decision analysis, such as Rock Engineering Systems (RES) and Analytic Hierarchical Process (AHP) are implemented for weighting and ranking landslide related factors in an objective manner. Through the use of GIS these approaches provide a highly accurate landslide susceptibility map. For this purpose and in order to automate the process, the Expert Knowledge for Landslide Assessment Tool (EKLATool) was developed as an extension tightly integrated in the ArcMap environment, using ArcObjects and Visual Basic script codes. The EKLATool was implemented in an area of Xanthi Prefecture, Greece, where a spatial database of landslide incidence was  available

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