scholarly journals Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

2021 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Hasnain Abbas ◽  
Aftab Ahmed Khan ◽  
Dostdar Hussain ◽  
Garee Khan ◽  
Syed Najam ul Hassan ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Main Route

scholarly journals Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

2020 ◽  
Vol 10 (18) ◽  
pp. 6335 ◽  
Author(s):  
Kamila Pawluszek-Filipiak ◽  
Natalia Oreńczak ◽  
Marta Pasternak
Keyword(s):  
Landslide Susceptibility ◽  
Research Question ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
High Susceptibility ◽  
Geological Structures ◽  
Area Index ◽  
Landslide Occurrence ◽  
Very High

To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

scholarly journals The importance of input data on landslide susceptibility mapping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Krzysztof Gaidzik ◽  
María Teresa Ramírez-Herrera
Keyword(s):  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Input Data ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Topographic Data ◽  
Susceptibility Maps ◽  
Landslide Mass ◽  
The Impact

AbstractLandslide detection and susceptibility mapping are crucial in risk management and urban planning. Constant advance in digital elevation models accuracy and availability, the prospect of automatic landslide detection, together with variable processing techniques, stress the need to assess the effect of differences in input data on the landslide susceptibility maps accuracy. The main goal of this study is to evaluate the influence of variations in input data on landslide susceptibility mapping using a logistic regression approach. We produced 32 models that differ in (1) type of landslide inventory (manual or automatic), (2) spatial resolution of the topographic input data, (3) number of landslide-causing factors, and (4) sampling technique. We showed that models based on automatic landslide inventory present comparable overall prediction accuracy as those produced using manually detected features. We also demonstrated that finer resolution of topographic data leads to more accurate and precise susceptibility models. The impact of the number of landslide-causing factors used for calculations appears to be important for lower resolution data. On the other hand, even the lower number of causative agents results in highly accurate susceptibility maps for the high-resolution topographic data. Our results also suggest that sampling from landslide masses is generally more befitting than sampling from the landslide mass center. We conclude that most of the produced landslide susceptibility models, even though variable, present reasonable overall prediction accuracy, suggesting that the most congruous input data and techniques need to be chosen depending on the data quality and purpose of the study.

scholarly journals UTILIZATION OF FINE RESOLUTION SATELLITE DATA FOR LANDSLIDE SUSCEPTIBILITY MODELLING: A CASE STUDY OF KASHMIR EARTHQUAKE INDUCED LANDSLIDES

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 25-30
Author(s):  
M. Z. Ali ◽  
H.-J. Chu ◽  
S. Ullah ◽  
M. Shafique ◽  
A. Ali
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Support Vector ◽  
Kashmir Earthquake ◽  
Validation Data ◽  
Mitigation Measure ◽  
Causative Factors

<p><strong>Abstract.</strong> The 2005 Kashmir earthquake has triggered thousands of landslides which devastated most of the livelihood and other infrastructure in the area. Landslide inventory and subsequently landslide susceptibility mapping is one of the main prerequisite for taking mitigation measure against landslide effects. This study has focused on developing most updated and realistic landslide inventory and Susceptibility mapping. The high resolution data of Worldveiw-2 having spatial resolution of 0.4 m is used for landslide inventory. Support Vector Machine (SVM) classifier was used for landslide inventory developing. Total 51460 number of landslides were classified using semi-automatic technique with covering area of 265 Km<sup>2</sup>, smallest landslide mapped is covering area of 2.01 m<sup>2</sup> and the maximum covered area of single landslide is 3.01 Km<sup>2</sup>. Nine influential causative factors are used for landslide susceptibility mapping. Those causative factors include slope, aspect, profile curvature, elevation, distance from fault lines, distance from streams and geology. Logistic regression model was used for the Landslides susceptibility modelling. From model the highest coefficient was assigned to geology which shows that the geology has higher influence in the area. For landslide susceptibility mapping the 70 % of the data was used and 30% is used for the validation of the model. The prediction accuracy of the model in this study is 92 % using validation data. This landslide susceptibility map can be used for land use planning and also for the mitigation measure during any disaster.</p>

scholarly journals A One-Class-Classifier-Based Negative Data Generation Method for Rapid Earthquake-Induced Landslide Susceptibility Mapping

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuai Chen ◽  
Zelang Miao ◽  
Lixin Wu ◽  
Anshu Zhang ◽  
Qirong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Training Data ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Data Generation ◽  
Negative Data ◽  
Positive Data ◽  
One Class Classifier

Machine learning with extensively labeled training samples (e.g., positive and negative data) has received much attention in terms of addressing earthquake-induced landslide susceptibility mapping (LSM). However, the extensive amount of labeled training data required by machine learning, particularly the precise negative data (i.e., non-landslide area), cannot be easily and efficiently collected. To address this issue, this study presents a one-class-classifier-based negative data generation method for rapid earthquake-induced LSM. First, an incomplete landslide inventory (i.e., positive data) was produced with the aid of change detection using before-and-after satellite images and the Geographic Information System (GIS). Second, a one-class classifier was utilized to compute the probability of landslide occurrence based on the incomplete landslide inventory followed by the negative data generation from the low landslide susceptibility areas. Third, the positive data as well as the generated negative data (i.e., non-landslide) were compounded to train a traditional binary classifier to produce the final LSM. Experimental results suggest that the proposed method is capable of achieving a result that is comparable to methods using the complete landslide inventory, and it displays good correspondence with recent landslide events, making it a suitable method for rapid earthquake-induced LSM. The findings in this study would be useful in regional disaster planning and risk reduction.

scholarly journals GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

2010 ◽  
Vol 10 (9) ◽  
pp. 1851-1864 ◽  
Author(s):  
F. Mancini ◽  
C. Ceppi ◽  
G. Ritrovato
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Geographical Information ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Multivariate Statistical ◽  
Causal Factors ◽  
Landslide Occurrence ◽  
Local Comparison

Abstract. This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

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