A Comparative Analysis of Binary Logistic Regression and Analytical Hierarchy Process for Landslide Susceptibility Assessment in the Dobrov River Basin, Romania

Patriche, Cristian Valeriu; Pîrnău, Radu Gabriel; Grozavu, Adrián; Roșca, Bogdan

doi:10.1016/s1002-0160(15)60047-9

Cited by 43 publications

(23 citation statements)

References 71 publications

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“…Numerous methods have been produced to assess landslide susceptibility at a regional scale, including direct geomorphological mapping (analysis of landslide inventories), heuristic approaches, statistical methods, physically-based models [7][8][9], and newly-developed machine learning models [10,11]. More detailed information of different models for landslide susceptibility mapping can be found in the literature [8,[12][13][14][15][16][17][18][19][20][21][22][23]. However, all the methods have both advantages and drawbacks, and no one method is accepted universally for the effective assessment of landslide hazards due to the complex nature of landslides [11].…”

Section: Introductionmentioning

confidence: 99%

“…However, all the methods have both advantages and drawbacks, and no one method is accepted universally for the effective assessment of landslide hazards due to the complex nature of landslides [11]. Patriche et al [15,16] compared methods of logistic regression and other different methods for landslide susceptibility, and that reported logistic regression was the best. Yilmaz [14,17] compared many methods from conventional models to new machine learning models, and found that the artificial neural network was the best.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Zhang

Zhao

et al. 2017

IJGI

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Abstract:Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence in coal mine areas. Landslide data collected by the Bureau of Land and Resources are represented by the X, Y coordinates of its central point; causative factors were calculated from topographic and geologic maps, as well as satellite imagery. The five-fold cross-validation method was adopted and the landslide/non-landslide datasets were randomly split into a ratio of 80:20. From this, five subsets for 20 times were acquired for training and validating models by GIS Geostatistical analysis methods, and all of the subsets were employed in a spatially balanced sample design. Three landslide models were built using support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models by selecting the median of the performance measures. Then, the three fitted models were compared using the area under the receiver operating characteristics (ROC) curves (AUC) and the performance measures. The results show that the prediction accuracies are between 73.43% and 87.45% in the training stage, and 67.16% to 73.13% in the validating stage for the three models. AUCs vary from 0.807 to 0.906 and 0.753 to 0.944 in the two stages, respectively. Additionally, three landslide susceptibility maps were obtained by classifying the range of landslide probabilities into four classes representing low (0-0.02), medium (0.02-0.1), high (0.1-0.85), and very high (0.85-1) probabilities of landslides. For the distributions of landslide and area percentages under different susceptibility standards, the SVM model has more relative balance in the four classes compared to the LR and the ANN models. The result reveals that the SVM model possesses better prediction efficiency than the other two models. Furthermore, the five factors, including lithology, distance from the road, slope angle, elevation, and land-use types, are the most suitable conditioning factors for landslide susceptibility mapping in the study area. The mining disturbance factor has little contribution to all models, because the mining method in this area is underground mining, so the mining depth is too deep to affect the stability of the slopes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Zhang

Zhao

et al. 2017

IJGI

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“…Thematic layers were prepared for five factors that we consider relevant for landslide manifestation in the entire region of the Moldavian Plateau [11]: altitude, slope, aspect, land use, and lithology ( Fig. 2).…”

Section: Methodsmentioning

confidence: 99%

“…The application of AHP methodology is based on previous research on the Bârlad Plateau [11]. The weights of the five previously selected factors were assigned on the basis of frequency of landslides [4].…”

Section: Methodsmentioning

confidence: 99%

Application of Ahp Method for Mapping Slope Geomorphic Phenomena

Grozavu

Patriche

Mihai

2017

Preprint

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Landslides are common and frequent geomorphic phenomena for the plateau regions in Romania having important consequences, especially economic ones, that needs designing scientific and technical plans for landslide risk mitigation. For this, an important preliminary step is assessing and mapping the landslide susceptibility. This paper examines a plateau zone in eastern Romania providing such a map, based on the landslides inventory, the digital elevation model (DEM) and the thematic layers of several factors thought to be potential predictors of landslides occurrence: topographic features, land use, and lithology. The methodological framework is based on the analytical hierarchy process (AHP) principles and factors weights attributed based on frequency of landslides. The predictive performance of the model was assessed using the confusion matrix, the ROC (receiver operating characteristic) curve and the AUC (area under curve) parameter. The results indicate a good correspondence between the susceptibility estimated for the test samples and for the validation samples

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“…The second general approach is statistical and thus does not posit mechanisms that control slope failure, but assumes rather that occurrences of past landslides can be related arbitrarily to measurable characteristics of the landscape [30][31][32]. In turn, these characteristics can be used to predict future landslide occurrence and then many common algorithms were applied including weighted linear combination (WLC), multiple regression model [33][34][35], artificial neural network model [36,37], and support vector machine [38,39]. All these statistical methods could properly present the probability distribution at spatial scale and show a prefect effect in practice.…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Wang

Bai

et al. 2018

Water

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Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.

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A Comparative Analysis of Binary Logistic Regression and Analytical Hierarchy Process for Landslide Susceptibility Assessment in the Dobrov River Basin, Romania

Cited by 43 publications

References 71 publications

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Application of Ahp Method for Mapping Slope Geomorphic Phenomena

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

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