A data-based landslide susceptibility map of Africa

Broeckx, Jente; Vanmaercke, Matthias; Duchateau, Rica; Poesen, Jean

doi:10.1016/j.earscirev.2018.05.002

Cited by 135 publications

(125 citation statements)

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“…What is known is that many geological and topographic conditions can influence the occurrence of landslides, such as formation lithology, faults, hydrology, attitude, slope angle, soil, and vegetation [8,9]. 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].…”

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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“…This highly populated region is characterized by high rainfall intensities, recent seismicity, deeply weathered substrates, and a complex rift topography [33,47,[49][50][51][52]. These factors render the area highly susceptible to landsliding [45,49]. Indeed, recent studies incorporating observations from satellite images and fieldwork reveal high landslide activity, with hundreds of recent landslide events over the last 10 years accounting for more than 5000 individual landslides mapped in the area [44,49,50,53].…”

Section: Underreported Landslide Events In the Wearmentioning

confidence: 99%

“…Water 2019, 11, x FOR PEER REVIEW 4 of 22 Figure 1. Landslide susceptibility at 0.25° resolution, derived from (a) the continental-scale model of [45] and (b) the regional-scale model of [49], and distribution of dated and localized landslide events in the western branch of the East African Rift. Landslide events used for calibration (CAL) are shown in blue and those for validation (VAL) in green, with their respective number between brackets.…”

Section: Underreported Landslide Events In the Wearmentioning

confidence: 99%

“…Identifying thresholds for rainfall-triggered landsliding in data-scarce environments is challenging with respect to information on landslide occurrence and hydrophysical parameters, resulting in the quasi-absence of research on this topic in regions such as Central Africa [3] despite high hazard potential [29,[44][45][46][47]. The AR-S approach allowed defining the first regional threshold for landsliding in the western branch of the East African Rift (WEAR) [35].…”

Section: Introductionmentioning

confidence: 99%

“…To the authors' knowledge, it has so far not been used in other regions. Moreover, the cited study relied on limited data available on landslide occurrence, global satellite-based rainfall estimates [48], and continental susceptibility data [45]. There is hence a strong need for testing the method's robustness with other data sets.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding

Monsieurs

Dewitte

Depicker

et al. 2019

Water

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Determining rainfall thresholds for landsliding is crucial in landslide hazard evaluation and early warning system development, yet challenging in data-scarce regions. Using freely available satellite rainfall data in a reproducible automated procedure, the bootstrap-based frequentist threshold approach, coupling antecedent rainfall (AR) and landslide susceptibility data as proposed by Monsieurs et al., has proved to provide a physically meaningful regional AR threshold equation in the western branch of the East African Rift. However, previous studies could only rely on globaland continental-scale rainfall and susceptibility data. Here, we use newly available regional-scale susceptibility data to test the robustness of the method to different data configurations. This leads us to improve the threshold method through using stratified data selection to better exploit the data distribution over the whole range of susceptibility. In addition, we discuss the effect of outliers in small data sets on the estimation of parameter uncertainties and the interest of not using the bootstrap technique in such cases. Thus improved, the method effectiveness shows strongly reduced sensitivity to the used susceptibility data and is satisfyingly validated by new landslide occurrences in the East African Rift, therefore successfully passing first transferability tests. parameters through a spatially extended infinite-slope stability model [25]. However, the large required data input for well-calibrated process-based thresholds explains their current limitation to mostly applications at the hillslope scale or through numerical simulations [4,21,[25][26][27].The estimation of empirical rainfall thresholds is also associated with additional sources of uncertainty. Firstly, landslide inventories are inherently biased towards high-impact landslide events and regions that are most accessible, while their accuracy is constrained by the scientific validity of the reporting sources, especially in data-scarce low-capacity environments [1,[28][29][30][31]. Secondly, rainfall data comprise uncertainties related to the spatial representativeness of rain gauges or biases in satellite-derived estimates [32,33]. Thirdly, the definition of rainfall parameters, with intensity and duration forming the most frequently used parameter couple [3,5], varies strongly across studies [3]. Finally, the latter parameters' interdependence is problematic, obscuring the physical processes associated with the calculated thresholds [34].In order to account for and characterize threshold uncertainties, a growing number of reproducible statistical techniques have been developed [3]. A weakness of such methods is, however, that they are generally tailored to a specific area and available data sets, which often prevents straightforward transferability to other regions and data sets [35]. Nevertheless, transferability is not only essential for evaluating and comparing landslide hazard over different regions of the world [10,36], but also valuable in the context of the in...

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Relations Between Soil Development and Landslides

Temme

2021

Hydrogeology, Chemical Weathering, and Soil Formation

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A data-based landslide susceptibility map of Africa

Cited by 135 publications

References 81 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

Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding

Relations Between Soil Development and Landslides

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