GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia

Meten, Matebie; Bhandary, Netra Prakash; Yatabe, Ryuichi

doi:10.1007/s11629-015-3464-3

Cited by 85 publications

(83 citation statements)

References 35 publications

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“…Logistic regression (LR) model believes several physical parameters may affect the probability of landslide occurrence [117][118][119]. The LR model allows developing a multivariate regression correlation between a dependent variable and several independent variables [120,121]. The advantage of LR model is that through the addition of suitable link function to the usual linear regression model, the variables can be continuous, discontinuous, or both, and they do not to obey normal distribution [122,123].…”

Section: Logistic Regressionmentioning

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: Logistic Regressionmentioning

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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“…There is no standard method for selecting these factors (Yalcin, 2008). In the present case, seven factors are considered for landslide hazard evaluation based on the landslide occurrences and their characteristics in Tarmaber, and published information (Abay and Barbieri, 2012; Meten et al, 2015). These include lithology, proximity to fault, land use, slope steepness, aspect, elevation and proximity to drainage.…”

Section: Input Data Preparationmentioning

confidence: 99%

GIS-based Landslide Susceptibility Evaluation Using Analytical Hierarchy Process (AHP) Approach: The Case of Tarmaber District, Ethiopia

Abay

Barbieri

Woldearegay

2019

mejs

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Landslides are one of the natural threats that often result in great loss of life and destruction of property in Ethiopia. One of the areas that is affected by landslides of different types and sizes is the Tarmaber district in the rift margin in the central part of Ethiopia. Keeping in view the cause and effect relationship and mitigation, Analytical Hierarchy Process (AHP) approach is used in the present case to understand the possible causes for landslides. Based on AHP, landslide susceptibility map is produced for Tarmaber using field survey data, remote sensing data, and geographic information system tools. The factors considered in the present case that can influence landslides are lithology, proximity to fault, land use, proximity to drainage, slope gradient, aspect and elevation. The results are validated with the inventory of landslide occurrences. The landslide susceptibility index (LSI) is calculated using the weighted -linear combination (WLC) technique based on the assigned weight and rating given by the AHP method. The accuracy of the results verified using the existing landslide locations is about 88.6%.The landslide susceptibility zonation map has identified our classes/zones: very high (29%), high (44%), moderate (20.0%) and low (7%).

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“…Based on FR analysis, the relationship between the factors and gully locations were assessed. Conditioning-factor classes with FR values >1 indicated areas with greater gully-erosion susceptibility [82]. Elevation plays an important role in vegetation and precipitation type and, therefore, controls the spatial distribution and gully erosion processes [99].…”

Section: Discussionmentioning

confidence: 99%

“…Logistic regression (LR) is one of the most popular multivariate statistical analysis methods [78][79][80]. It can make a multivariate regression correlation between a dependent variable and several independent variables [81,82]. The advantage of LR is that the variables can be continuous, discontinuous, or a combination of the two [83,84].…”

Section: Logistic Regressionmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Water

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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately Water 2020, 12, 16 2 of 26 identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.

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GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia

Cited by 85 publications

References 35 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

GIS-based Landslide Susceptibility Evaluation Using Analytical Hierarchy Process (AHP) Approach: The Case of Tarmaber District, Ethiopia

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

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