Development and application of landslide susceptibility analysis techniques using geographic information system (GIS)

Abstract

The purpose of this study is to develop landslide susceptibility analysis techniques using Geographic information system (GIS) and apply the newly developed techniques for assessment of landslide susceptibility to two study areas of Yongin and Janghung, Korea Landslide locations detected from interpretation of aerial photo and field survey, and topographic, soil, forest, and geological maps of the study area, Yongin, were collected. The data on the locations of landslide, topography, soil, forest and geology were constructed into spatial database using GIS and landslide related 14 factors were extracted from the databases. Finally landslide susceptibility was analyzed using the landslide related factors by probability, logistic regression and neural network methods.In the other study area, Janghung, landslide locations were detected from IRS satellite images by change detection technique, and geological structure of foliation were surveyed and analyzed. As the landslide occurrence factors, location of landslide, geological structure and topography were constructed into spatial database using GIs. With the landslide occurrence factors, landslide susceptibility was assessed by the relation analysis between strike and dip of the foliation, and aspect and slope of the topography.The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and landslide location data. GIS was used to analyze vast data efficiently and statistical and neural network programs were used to maintain specialty and accuracy. The result can be used to prevention of hazard, land use planning and construction planning as basic data.

Quantification of the Ecological Value of Railroad Development Areas Using Logistic Regression Analysis

“…It has also been used for quantitative evaluation in large or inaccessible areas. Logistic regression analysis has been used to evaluate ecological factors related to various topics [6,[12][13][14][15][16][17]. In related case studies, logistic regression analysis was used in various fields, such as landslide risk assessment prediction [12,14,16], wild boar habitat model development [13], amphibians habitat suitability model [6], and forest impact assessment in North Korea [5,15].…”

“…Logistic regression analysis has been used to evaluate ecological factors related to various topics [6,[12][13][14][15][16][17]. In related case studies, logistic regression analysis was used in various fields, such as landslide risk assessment prediction [12,14,16], wild boar habitat model development [13], amphibians habitat suitability model [6], and forest impact assessment in North Korea [5,15]. However, as the subject of this study, there are no examples of use in the environmental evaluation of railway projects.…”

Assessing frequency ratio method for landslide susceptibility mapping in Cameron Highlands, Malaysia

“…There were several studies on the comparison of different methods to obtain the best susceptibility mapping results. One of these is the work by Lee et al [4] which evaluated the probability method, logistic regression method and neural network method and concluded that probability is the most simple method and the output can be understood easily. Another work by Chuanhua and Xueping [5] analyzed the results between weight of evidence method and information method.…”

Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms

“…Worldwide, there have been many studies conducted on LSMs using different methods and models, including statistical methods using GIS [12]; LR model and probability methods [13]; the role of topography and geological structure [14]; a GIS oriented multivariate statistical assessment [15]; comparison of multivariate (logical regression) and bivariate methods using GIS procedures [16]; GIS and field survey [17]; a probability-frequency ratio model [18]; a binary logistic regression [19]; qualitative multi-criteria analysis [20]; LR versus artificial neural networks (ANN) [21]; decision tree [22]; neural network based model [23]; LR [24]; analytical hierarchy process (AHP) [25]; RF algorithm [26]; AHP, frequency ratio, and LR models [27]; frequency ratio, LR and fuzzy logic methods [10]; kernel-based Gaussian process, support vector machine (SVM), and LR [28]; a hybrid of ANN and ensemble algorithms [29]; ANFIS (adaptive neuro-fuzzy inference system), SVM, generalized additive model, and frequency ratio [30]; SVM and index of entropy models [6]; Bayes' net, radical basis function (RBF) classifier [31]; RF with the bivariate statistical method, index of entropy model, and certainty factor [32]. Recently, machine learning algorithms (MLAs) have been introduced to have better findings than conventional techniques in many areas, especially in natural hazard studies such as floods [33][34][35][36][37][38][39][40], wildfire [41], sinkholes [42], drought [43], earthquakes [44,…”