Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam)

Hung, Le Quoc; Van, Nguyen Thi Hai; Duc, Do Minh; Ha, Le Thi Chau; Son, Pham Van; Khanh, Nguyen Ho; Binh, Luu Thanh

doi:10.1007/s10346-015-0657-3

Cited by 75 publications

(33 citation statements)

References 25 publications

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“…As one of the most frequently-occurring geological disasters in the world, landslides have triggered a series of threats to human society including casualties, economic losses, infrastructure destruction, and geological environment problems [1][2][3]. Therefore, to reduce the losses, it is absolutely necessary to study the landslide susceptibility in a region [4,5]. According to the previous researches, landslide susceptibility can be roughly defined as the landslide occurrence probability in an area under the synergistic effect of a number of regional geological environmental factors [6,7].…”

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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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 investigations on landslide hazard and risk have evolved since the last two decades thank to the advancement of GIS and remote sensing technology (Hung et al 2016). However, like in many other developing countries, as summarized by van Westen et al (2006), there are many kinds of obstacles that make the execution of hazard risk zonation difficult in Vietnam.…”

Section: Introductionmentioning

confidence: 99%

“…However, like in many other developing countries, as summarized by van Westen et al (2006), there are many kinds of obstacles that make the execution of hazard risk zonation difficult in Vietnam. Most of projects on landslide mapping in Vietnam have been conducted at small scales (less than 1:100,000) in large regions (more than 1000 km 2 ) (Hung et al 2016). Several advanced-worldwide technologies for landslide zonation and warning at larger scales (>1:50,000) have been applied to several pilot areas, but not yet systematically adapted to the Vietnamese context (Hung et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Landslide Inventory Mapping in the Fourteen Northern Provinces of Vietnam: Achievements and Difficulties

Hung

Van

Son

et al. 2017

Advancing Culture of Living With Landslides

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The State-Funded Landslide Project (SFLP) is a national program to systematically assess landslide susceptibility, hazard and risk for all of prone areas in Vietnam. Under this SFLP, in the first phase of SFLP (2012SFLP ( -2014, activities of landslide inventory mapping were implemented over the fourteen Northern mountainous provinces. As the achievements, 10,149 historic landslides were mapped by field surveys and 9405 locations with landslide signs were interpreted from air-photos and analysis of 3D relief. Approximately 83% of the surveyed landslides locate in accessible areas, with small and medium dimensions and partly as a result of the slope cuts. About 76% of the interpreted landslides locate in inaccessible areas, and only 24% of the interpreted locate in accessible areas, of which 65% were found active landslides at the time of surveying, naturally occurred with large dimensions. However, the inventory exposes some major drawbacks: (1) The unavailability of multi-date air-photos; (2) The lack of human resources with enough experiences in image interpretation; (3) The difficulties of verifying the interpreted landslides, especially for the inaccessible sites; (4) Few or no sources of historic information due to the isolated sites or little memory of small or medium size events; (5) No updates developed by the surveyors after they finished their tasks. Those drawbacks can lead to the insufficiency of adequate data on the types, sizes and characteristics of the slope failures, especially the exact dates of occurrences. Despite of those difficulties, the achieved inventory database have been updated and then used as basic input for the susceptibility and hazard mapping as well as preliminary results of SFLP to inform the local authorities and communities about real situations of landslides in their areas.

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“…The multivariate statistical methods evaluate the combined relationship between a dependent variable (landslide occurrence) and a series of independent variables (landslide controlling factors), and the most popular methods to analyze the resulting matrix include logistic regression (LR) [6,13,[28][29][30][31][32][33], discriminant analysis (DA) [34,35], random forest (RF) [36][37][38] and active learning statistical analysis, such as the artificial neural networks (ANNs) [3,6,[39][40][41][42].Physically based methods, such as deterministic techniques, are based on mathematical modeling of the physical mechanisms controlling slope failure [43][44][45][46][47][48][49]. However, it is reported that the methods are only applicable over large areas when the geological and geomorphological conditions are fairly homogeneous and the landslide types are simple [17].Moreover, several studies have used two or more models to produce landslide susceptibility maps and compare their accuracy [3,15,16,36,[50][51][52][53].Although many approaches are available for producing landslide susceptibility maps, the focus in this paper is on the influence of different knowledge-driven methods for a series of parameters on the accuracy of LSM. A comparative study between the ICM using three knowledge-driven methods, that is, rough set (RS), artificial hierarchy process (AHP), and entropy weight method (EWM), has been considered so far in the literature.…”

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confidence: 99%

“…Moreover, several studies have used two or more models to produce landslide susceptibility maps and compare their accuracy [3,15,16,36,[50][51][52][53].…”

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confidence: 99%

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Qin

Cao

et al. 2019

Entropy

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Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.Entropy 2019, 21, 372 2 of 24 involving land use management as an efficient approach to reduce property damage and economic loss in landslide-prone areas [1,[6][7][8][9]. The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.Landslide susceptibility maps can be obtained using both qualitative (inventory-based and knowledge-driven methods) or quantitative approaches (data-driven methods and physically based models) [4,[10][11][12][13][14][15][16][17].Landslide inventory-based techniques, as a prelude to all other methods, include the collection of past landslide data, construction of databases, and production of susceptibility maps based on those data [18]. Landslide inventory mapping can be carried out using a variety of methods that were updated and summarized by Corominas et al. [17].Knowledge-driven methods that estimate landslide potential from the practical experience and expertise of the researcher are used by geomorphologists to analyze...

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Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam)

Cited by 75 publications

References 25 publications

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

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

Landslide Inventory Mapping in the Fourteen Northern Provinces of Vietnam: Achievements and Difficulties

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

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