Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)

Myronidis, Dimitriοs; Papageorgiou, C.; Theophanous, Stavros

doi:10.1007/s11069-015-2075-1

Cited by 154 publications

(58 citation statements)

References 37 publications

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“…The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings [56]. The false-positive values along the x-axis are the proportion …”

Section: Receiver Operating Characteristics Curvementioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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Section: Receiver Operating Characteristics Curvementioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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“…As landslide susceptibility mapping is the first and foremost step in landslide prevention, numerous researchers have been devoted to landslide susceptibility mapping in past years [10][11][12][13][14]. In general, the methods used in previous studies can be roughly divided into two types: qualitative and quantitative, for example, analytic hierarchy process (AHP) is the most commonly-used qualitative approach in landslide susceptibility mapping [14][15][16]. In recent…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.Symmetry 2019, 11, 762 2 of 24years, machine learning method has been gradually applied in landslide susceptibility mapping researches, such as artificial neural network (ANN) [17][18][19], support vector machine (SVM) [20][21][22], logistic model tree (LMT) [23,24], rotation forest (RF) [25,26], classification and regression tree (CART) [27,28], adaptive neuro-fuzzy inference systems (ANFIS) [29,30], and genetic algorithm (GA) [31,32]. Furthermore, statistical approach is another widely-used model which can also be divided into two types: bivariate and multivariate. In statistical approaches, the weight of each class of every factor was calculated by overlying the landslide inventory map and landslide conditioning factors map [33,34]. The frequently-used statistical approaches are frequency ratio (FR) [35][36][37], logistic regression (LR), evidential belief function (EBF) [38,39], weights of evidence (WoE) [40,41], certainty factor (CF) [42,43], and information value (IV) [44,45].In this paper, three bivariate statistical methods, namely weight of evidence (WoE), evidence belief function (EBF), index of entropy (IoE), coupled with logistic regression (LR) were introduced to carry out the landslide susceptibility mapping in Muchuan County, which lies in the southeast of Sichuan Province, China. All the approaches were first applied in Muchuan County and the main purpose of this article is to obtain the landslide susceptibility maps through the above models. The main difference between this study and literatures referenced above is to achieve accurate landslide susceptibility maps for...

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“…However, some techniques better suit to particular decision problems than others do (Mergias et al 2007;Dagdeviren et al 2009). The most popular methods are scoring models (Nelson 1986), AHP (Ecer 2014;Ivlev et al 2014;Myronidis et al 2016;Singh, Nachtnebel 2016), Analytic Network Process (ANP), Axiomatic Design (AD) (Khandekar et al 2015), Utility Models (Munoz, Sheng 1995), TOPSIS (Liu 2009;Antuchevičiene et al 2010;Maimoun et al 2016), Elimination and Choice Translating Reality (ELECTRE) (Wang, Triantaphyllou 2008) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) (Kabak, Dağdeviren 2014). These MCDM methods can be classified in many ways.…”

Section: Definitions and Preliminaries: Mcdmmentioning

confidence: 99%

A Comparative Study of the Capability of Alternative Mixed Integer Programming Formulations

Keçeci

Derya

Dinler

et al. 2017

Technological and Economic Development of Economy

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Abstract. In selecting the best mixed integer linear programming (MILP) formulation the important issue is to figure out how to evaluate the performance of each candidate formulation in terms of selected criteria. The main objective of this study is to propose a systematic approach to guide the selection of the best MILP formulation among the alternatives according to the needs of the decision maker. For this reason we consider the problem of "selecting the most appropriate MILP formulation for a certain type of decision maker" as a multi-criteria decision making problem and present an integrated AHP-TOPSIS decision making methodology to select the most appropriate formulation. As an example the proposed decision making methodology is implemented on the selection of the MILP formulations of the Capacitated Vehicle Routing Problem (CVRP). A numerical example is provided for illustrative purposes. As a result, the proposed decision model can be a tool for the decision makers (here they are the scientists, engineers and practitioners) who intend to choose the appropriate mathematical model(s) among the alternatives according to their needs on their studies. The integrated AHP-TOPSIS approach can simply be incorporated into a computer-based decision support system since it has simplicity in both computation and application.

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Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)

Cited by 154 publications

References 37 publications

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

A Comparative Study of the Capability of Alternative Mixed Integer Programming Formulations

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