scholarly journals Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping

2021 ◽  
Vol 9 ◽  
Author(s):  
Ting Xiao ◽  
Lanbing Yu ◽  
Weiming Tian ◽  
Chang Zhou ◽  
Luqi Wang
Keyword(s):  
Correlation Analysis ◽  
Spatial Correlation ◽  
Landslide Susceptibility ◽  
Causal Factor ◽  
Susceptibility Map ◽  
Susceptibility Assessment ◽  
Local Correlation ◽  
Causal Factors ◽  
Landslide Susceptibility Assessment ◽  
Class 1

A landslide susceptibility map (LSM) is the basis of hazard and risk assessment, guiding land planning and utilization, early warning of disaster, etc. Researchers are often overly keen on hybridizing state-of-the-art models or exploring new mathematical susceptibility models to improve the accuracy of the susceptibility map in terms of a receiver operator characteristic curve. Correlation analysis of the causal factors is a necessary routine process before susceptibility modeling to ensure that the overall correlation among all factors is low. However, this overall correlation analysis is insufficient to detect a high local correlation among the causal factor classes. The objective of this study is to answer three questions: 1) Is there a high correlation between causal factors in some parts locally? 2) Does it affect the accuracy of landslide susceptibility assessment? and 3) How can this influence be eliminated? To this aim, Wanzhou County was taken as the test site, where landslide susceptibility assessment based on 12 causal factors has been previously performed using the frequency ratio (FR) model and random forest (RF) model. In this work, we conducted a local spatial correlation analysis of the “altitude” and “rivers” factors and found a sizeable spatial overlap between altitude-class-1 and rivers-class-1. The “altitude” and “rivers” factors were reclassified, and then the FR model and RF model were used to reevaluate the susceptibility and analyze the accuracy loss caused by the local spatial correlation of the two factors. The results demonstrated that the accuracy of LSMs was markedly enhanced after reclassification of “altitude” and “rivers,” especially for the RF model–based LSM. This research shed new light on the local correlation of causal factors arising from a particular geomorphology and their impact on susceptibility.

scholarly journals Landslide susceptibility assessment of the Chure Khola Catchment area of the Siwalik region, Central Nepal

2018 ◽  
Vol 56 (1) ◽  
pp. 19-30
Author(s):  
Prakash Gyawali ◽  
Naresh Kazi Tamrakar
Keyword(s):  
Landslide Susceptibility ◽  
Catchment Area ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Early Pleistocene ◽  
Susceptibility Map ◽  
Susceptibility Assessment ◽  
Statistical Index ◽  
Landslide Susceptibility Assessment ◽  
Siwalik Hills

Landslide susceptibility analysis is carried out in the Chure Khola Catchment, between Amlekhganj and the Churia Mai Range of the Bara District, covering area of 20 sq. km. The catchment lies in the Siwalik Hills composing the Siwalik Group of rocks of Middle Miocene to Early Pleistocene age. Owing to the week and fragile geology, the Siwalik Hills are prone to the gully erosion, shallow landslide and debris flow, during the heavy rain storms in monsoon seasons. In the present study, landslide susceptibility assessment was carried out using two methods, rapid field-based assessment and statistical index methods. For the susceptibility mapping of the river bank slopes, field- based method was used. The seven parameters such as slope angle, slope material, reduction to groundwater, effect of drainage, effect of past failure, effect of vegetation cover and effect of land use were used to calculate the factor of safety in the field. The slope areas were classified as highly susceptible (FS<0.7), susceptible (0.7<FS<1), marginally stable (1<FS<1.2) and stable (FS>1.2) categories, and finally, a susceptibility map was prepared. For the total 4.179 sq. km area where rapid field-based assessment was carried out, the areas covered by highly susceptible, susceptible, marginally stable and stable zones are respectively, 21.56%, 22.11%, 17.37% and 38.95%. Among the highly susceptible and susceptible zones identified, 71% sites have experienced recent slope failures. Landslide susceptibility mapping of the whole catchment area was prepared using statistical index method, and considering seven causative parameters such as elevation, slope, slope aspect, curvature, river proximity, stream density and lithology, which were determined and prepared from DEM using Arc GIS. Eighty percent landslides were used as the training sample for the spatial analysis, whereas 20% landslides were used for the validation of the study. The landslide susceptibility map exhibits the areas covered by very high, high, moderate, low and very low susceptibility zones are 47.18%, 25.28%, 19.77%, 3.60% and 4.16%, respectively. Validity of the study was determined using Riemann Sums method. Success Rate Curve shows that 78.04 % of the areas lie under the curve. Evaluating susceptibility in small watershed is important to mitigate shallow landslide related problems and in rehabilitating forest areas in the Churiya Hills of Nepal.

scholarly journals Slide Type Landslide Susceptibility Assessment of the Büyük Menderes Watershed Using Artificial Neural Network Method

2021 ◽  
Author(s):  
Senem Tekin ◽  
Tolga Çan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Landslide Susceptibility ◽  
Operating Characteristic ◽  
Susceptibility Map ◽  
Susceptibility Assessment ◽  
Receiver Operating Characteristic Curves ◽  
Landslide Susceptibility Assessment ◽  
Network Method ◽  
Artificial Neural Network Method

Abstract The Büyük Menderes watershed is the largest drainage watershed in Western Anatolia with an area of approximately 26000 km2. In the study area, almost 863 landslides occurred, extending over 222 km2 with a mean landslide area of 0.21 km2. In this study, landslide susceptibility assessment was carried out using Artificial Neural Network method which is one of the data driven methods. Geology, digital elevation model, slope, topographic wetness index, roughness index, plan, profile curvatures, and proximity to the active faults and rivers were used as landslide conditioning factors. In susceptibility assessments, landslides were separated by 70 % analysis, 15 % test and validation data sets by random selection method. The performance of the landslide susceptibility map was assessed by the area under the receiver operating characteristic curves, error histogram, and confusion matrix, respectively. The area under the receiver operating characteristic curves, analysis, testing, validation, landslides and study ares was found 0.82, 0.84, 0.86, 0.82. The susceptibility map had a high perediction rate in which high and very high susceptible zones corresponded to 26 % of the study area including 82 % of the recorded landslides.

Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data

2021 ◽  
Author(s):  
Cahio Guimarães Seabra Eiras ◽  
Juliana Ribeiro Gonçalves de Souza ◽  
Renata Delicio Andrade de Freitas ◽  
César Falcão Barella ◽  
Tiago Martins Pereira
Keyword(s):  
Discriminant Analysis ◽  
Efficient Method ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment

scholarly journals Landslide Susceptibility Assessment of Mauritius Island (Indian Ocean)

Geosciences ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 493 ◽  
Author(s):  
Vincenzo Marsala ◽  
Alberto Galli ◽  
Giorgio Paglia ◽  
Enrico Miccadei
Keyword(s):  
Indian Ocean ◽  
Landslide Susceptibility ◽  
Integrated Approach ◽  
Geographical Information ◽  
Susceptibility Assessment ◽  
Gis Technology ◽  
Mountain Areas ◽  
Landslide Susceptibility Assessment ◽  
Hydrological Hazards ◽  
Mauritius Island

This work is focused on the landslide susceptibility assessment, applied to Mauritius Island. The study area is a volcanic island located in the western part of the Indian Ocean and it is characterized by a plateau-like morphology interrupted by three rugged mountain areas. The island is severely affected by geo-hydrological hazards, generally triggered by tropical storms and cyclones. The landslide susceptibility analysis was performed through an integrated approach based on morphometric analysis and preliminary Geographical Information System (GIS)-based techniques, supported by photogeological analysis and geomorphological field mapping. The analysis was completed following a mixed heuristic and statistical approach, integrated using GIS technology. This approach led to the identification of eight landslide controlling factors. Hence, each factor was evaluated by assigning appropriate expert-based weights and analyzed for the construction of thematic maps. Finally, all the collected data were mapped through a cartographic overlay process in order to realize a new zonation of landslide susceptibility. The resulting map was grouped into four landslide susceptibility classes: low, medium, high, and very high. This work provides a scientific basis that could be effectively applied in other tropical areas showing similar climatic and geomorphological features, in order to develop sustainable territorial planning, emergency management, and loss-reduction measures.

Landslide Susceptibility Assessment Mapping

2017 ◽  
pp. 493-512
Author(s):  
George D. Bathrellos ◽  
Dionissios P. Kalivas ◽  
Hariklia D. Skilodimou
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment
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