Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

Castro‐Camilo, Daniela; Lombardo, Luigi; Martin, P.; Dou, Jie; Huser, Raphaël

doi:10.1016/j.envsoft.2017.08.003

Cited by 129 publications

(72 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aspect indicates the downslope direction, it is also related to is related to the orientation of precipitation, exposure to sunshine and wind impact [40]. The relationship between aspect and landslide occurrences are identified in a number of studies [4,32]. Their studies indicate that aspect influences the distribution of landslide by the propagation direction of seismic waves.…”

Section: Slope Aspectmentioning

confidence: 99%

“…For example, the recent Hokkaido Eastern Iburi earthquake on 6 September 2018, about 80% of the fatalities are caused by the landslides alone [2]. Apart from the fatalities, they also cause huge economic losses by damaging properties such as buildings, bridges and roads; this trend is observed more than any other natural disasters, such as earthquakes, typhoons, heat waves, sinkhole collapses, floods and forest fires [3][4][5][6]. The increased amount of urbanization and economic development together with the unusual frequency of severe regional precipitations owing to global climate change, the landslide hazard losses are expected to rise in the future [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Landslide susceptibility mapping (LSM) is regarded as a prime step for in the implementation of immediate disaster management planning and risk mitigation measures [4,6,[10][11][12]. Most LSM models issued hitherto have been targeted at single-type-induced landslides [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

et al. 2019

Self Cite

View full text Add to dashboard Cite

Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

show abstract

Section: Slope Aspectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Slope aspect affects the slope stability indirectly via hydrologic processes, such as the direction of rainfall and sunshine [64][65][66]. In addition, the slope aspect can also influence the weathering process and vegetation distribution [67,68]. In this study, the slope was classified into nine categories, i.e.…”

Section: Landslide Conditioning Factorsmentioning

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

View full text Add to dashboard Cite

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...

show abstract

“…Bivariate and multivariate approaches are the most common statistical techniques, though probabilistic (e.g. Lee 2005;Goetz et al 2011Goetz et al , 2015Youssef et al 2016;Castro Camilo et al 2017) and Machine-Learning approaches (e.g. Lee et al 2004;Ermini et al 2005;Marjanovic et al 2011;Pham et al 2017) are also used in this type of analyses.…”

Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

In the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.

show abstract

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

Cited by 129 publications

References 67 publications

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

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

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Contact Info

Product

Resources

About