Using Statistical Learning Algorithms in Regional Landslide Susceptibility Zonation with Limited Landslide Field Data

Regional Landslide Susceptibility Zonation(LSZ) is always challenged by the available amount of field data, especially in southwestern China where large mountainous areas and limited field information coincide. Statistical learning algorithms are believed to be superior to traditional statistical algorithms for their data adaptability. The aim of the paper is to evaluate how statistical learning algorithms perform on regional LSZ with limited field data. The focus is on three statistical learning algorithms, Logistic Regression(LR), Artificial Neural Networks(ANN) and Support Vector Machine(SVM). Hanzhong city, a landslide prone area in southwestern China is taken as a study case. Nine environmental factors are selected as inputs. The accuracies of the resulting LSZ maps are evaluated through landslide density analysis(LDA), receiver operating characteristic(ROC) curves and Kappa index statistics. The dependence of the algorithm on the size of field samples is examined by varying the sizes of the training set. The SVM has proven to be the most accurate and the most stable algorithm at small training set sizes and on all known landslide sizes. The accuracy of SVM shows a steadilyincreasing trend and reaches a high level at a small size of the training set, while accuracies of LR and ANN algorithms show distinct fluctuations. The geomorphological interpretations confirm the strength of SVM on all landslide sizes. Our results show that the strengths of SVM in generalization capability and model robustness make it an appropriate and efficient tool for regional LSZ with limited landslide field samples.

Landslide Hazard Zonation Mapping in Ghat Road Section of Kolli Hills, India

Landslides are the most common natural disaster in hilly terrain which causes changes in landscape and damage to life and property. The main objective of the present study was to carry out landslide hazard zonation mapping on 1:50,000 scale along ghat road section of Kolli hills using a Landslide Hazard Evaluation Factor(LHEF) rating scheme. The landslide hazard zonation map has been prepared by overlaying the terrain evaluation maps with facet map of the study area. The terrain evaluation maps include lithology, structure, slope morphometry, relative relief, land use and land cover and hydrogeological condition. The LHEF rating scheme and the Total Estimated Hazard(TEHD) were calculated as per the Bureau of Indian Standard(BIS) guidelines(IS: 14496(Part-2) 1998) for the purpose of preparation of Landslide Hazard Zonation(LHZ) map in mountainous terrains. The correction due to triggering factors such as seismicity, rainfall and anthropogenic activities were also incorporated with Total Estimated Hazard to get final corrected TEHD. The landslide hazard zonation map was classified as the high, moderate and low hazard zones along the ghat road section based on corrected TEHD.

Hazard assessment of landslide disaster using information value method and analytical hierarchy process in highly tectonic Chamba region in bosom of Himalaya

The present study is focused on a comparative evaluation of landslide disaster using analytical hierarchy process and information value method for hazard assessment in highly tectonic Chamba region in bosom of Himalaya. During study, the information about the causative factors was generated and the landslide hazard zonation maps were delineated using Information Value Method(IV) and Analytical Hierarchy Process(AHP) using Arc GIS(ESRI). For this purpose, the study area was selected in a part of Ravi river catchment along one of the landslide prone Chamba to Bharmour road corridor of National Highway(NH^(-1)54 A) in Himachal Pradesh, India. A numeral landslide triggering geoenvironmental factors i.e. slope, aspect, relative relief, soil, curvature, land use and land cover(LULC), lithology, drainage density, and lineament density were selected for landslide hazard mapping based on landslide inventory. Landslide hazard zonation map was categorized namely "very high hazard, high hazard, medium hazard, low hazard, and very low hazard". The results from these two methods were validated using Area Under Curve(AUC) plots. It is found that hazard zonation map prepared using information value method and analytical hierarchy process methods possess the prediction rate of 78.87% and 75.42%, respectively. Hence, landslide hazardzonation map obtained using information value method is proposed to be more useful for the study area. These final hazard zonation maps can be used by various stakeholders like engineers and administrators for proper maintenance and smooth traffic flow between Chamba and Bharmour cities, which is the only route connecting these tourist places.

Landslide susceptibility analysis of Karakoram highway using analytical hierarchy process and scoops 3D

Landslides are prevalent,regular,and expensive hazards in the Karakoram Highway(KKH)region.The KKH connects Pakistan with China in the present China-Pakistan Economic Corridor(CPEC)context.This region has not only immense economic importance but also ecological significance.The purpose of the study was to map the landslide-prone areas along KKH using two different techniquesAnalytical Hierarchy Process(AHP)and Scoops 3 D model.The causative parameters for running AHP include the lithology,presence of thrust,land use land cover,precipitation,and Digital Elevation Model(DEM)derived variables(slope,curvature,aspect,and elevation).The AHP derived final landslide susceptibility map was classified into four zones,i.e.,low,moderate,high,and extremely high.Over 80%of the study area falls under the moderate(43%)and high(40%)landslide susceptible zones.To assess the slope stability of the study area,the Scoops 3 D model was used by integrating with the earthquake loading data.The results of the limit equilibrium analysis categorized the area into four groups(low,moderate,high,and extremely high mass)of slope failure.The areas around Main Mantle Thrust(MMT)including Dubair,Jijal,and Kohistan regions,had high volumes of potential slope failures.The results from AHP and Scoops 3 D techniques were validated with the landslides inventory record of the Geological Survey of Pakistan and Google Earth.The results from both the techniques showed similar output that coincides with the known landslides areas.However,Scoops 3 D provides not only susceptible zones but also the range of volume of the potential slope failures.Further,these techniques could be used in other mountainous areas,which could help in the landslide mitigation measures.

Landslide Hazard and Risk Assessment on the Northern Slope of Mt. Changbai, China

Landslide hazard and risk assessment on the northern slope of Mt. Changbai, a well-known tourist attraction near the North Korean-Chinese border, are assessed. This study is divided into two parts, namely, landslide hazard zonation and risk assessment. The 1992 Anbalagan and Singh method of landslide hazard zonation （LHZ） was modified and used in this area. In this way, an Associative Analysis Method was used in representative areas to get a measure for controlling factors （slope gradient, relative relief, vegetation, geology, discontinuity development, weak layer thickness and ground water）. For the membership degree of factor to slope failure, the middle range of limited values was used to calculate LHZ. Based on an estimation of the potential damage from slope failure, a reasonable risk assessment map was obtained using the relationship of potential damage and probable hazard to aid future planning and prediction and to avert loss of life.

Potential of GIS Evaluation for Mass Wasting Processes in Mountain Ranges of South West Coast of India

The aim of the study is to monitor and assess landslide hazards by remote sensing data processing and GIS （Geographic Information Service） spatial analysis. Idukki district, the western Ghats of India was chosen as test area, because of frequent destructive mass wasting processes. Western Ghats is a prominent orographic feature that runs parallel to the south west coast of India. Predicting landslide hazard on a regional scale, namely the assessment of actual and potential mass movement over large area is carried out using Remote Sensing and GIS. A numerical weightage to the causative factors of slope instability such as slope, relative relief, aspect, curvature, drainage density, drainage frequency, land use, road buffer and drainage buffer are assigned as per earlier workers for the purpose of landslide susceptibility zonation. A high degree of match is found between observed and predicted landslide hazard by the procedure employed in the study.