Characterization and quantification of path dependency in landslide susceptibility

Samia, Jalal; Temme, A.J.A.M.; Bregt, A.K.; Wallinga, Jakob; Guzzetti, Fausto; Ardizzone, Francesca; Rossi, Mauro

doi:10.1016/j.geomorph.2017.04.039

Cited by 82 publications

(54 citation statements)

References 42 publications

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“…Inspection of the model fitting ( Figure 7) and predictive ( Figure 11) performances further reveals that the more advanced models (Mod3, Mod4, Mod5) where generally better at predicting the spatial (i.e., "where") rather than the temporal (i.e., "when") component. We maintain that this is due to the combined effect of (i) the inherent short-term temporal viability-and related unpredictability-of landslide phenomena at the SU scale, at least in our study area (Samia et al, 2017a(Samia et al, ,b, 2018, and (ii) the number and length of the considered temporal periods and the number of landslides in each period (Figure 3), which depend on the temporal frequency of landslides in our study area. The latter, is in turn controlled by the frequency of the landslide triggering forcing events (e.g., severe or prolonged rainfall periods, rapid snow-melt events) (Rossi et al, 2010b;Witt et al, 2010).…”

Section: A New Landslide Predictive Modelling Frameworkmentioning

confidence: 86%

“…It is also known that landslides in the area do not occur randomly in time. As mentioned before, Samia et al (2017aSamia et al ( ,b, 2018, who worked in the same area, identified a landslide heritage effect that conditions the occurrence of new landslides dependent on the location of previous landslides over periods of less than 15 years. Our own results confirm that this heritage effect is limited in time, with only a minority of the SUs exhibiting a constant, long term clustering or repellency trend, with the vast majority of the SUs showing fluctuating dependence signals through time (Figure 8).…”

Section: Geomorphological Considerationsmentioning

confidence: 91%

“…We take this as empirical evidence of the fact that the temporal prediction of landslides over relatively long periods, longer than about 15 years in our case, is problematic and inherently uncertain. Samia et al (2017aSamia et al ( ,b, 2018, working in the same study area, identified a landslide heritage effect-which they called "landslide path dependency"-that conditions the occurrence of new landslides dependent on the location of previous landslides in the same SU, over periods of less than 15 years. Both empirical findings have consequences for hazard assessment.…”

Section: Hazard Considerationsmentioning

confidence: 99%

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Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

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Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a novel Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km 2 area into 889 slope units (SUs). In each SU, we computed the percentage of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model, and 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to a "traditional" landslide susceptibility model. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In partic...

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Section: A New Landslide Predictive Modelling Frameworkmentioning

confidence: 86%

Section: Geomorphological Considerationsmentioning

confidence: 91%

Section: Hazard Considerationsmentioning

confidence: 99%

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Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

Self Cite

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“…Plan curvature is considered as an important factor which controls the aggregation and separation of topography and has a direct influence on the velocity of water flow and thus erosion [41,56,57]. In this paper, plan curvature ranged from -26.54 to 26.21 and was reclassified into five 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

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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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“…), soil parameters (soil depth and soil type), land use/cover and hydrologic conditions (rainfall) in generating accurate landslide susceptibility maps. Furthermore, other features, such as slope length, topographical wetness index (TWI), topographic position index (TPI), the vertical distance to the nearest channel network, relative slope position and valley depth have been reported to play important roles in landslide susceptibility modeling (Chauhan et al, 2010;Costanzo et al, 2012;Pourghasemi et al, 2013;Yilmaz et al, 2013;Massimo Conforti et al, 2014;Samia et al, 2017;Vargas-Cuervo et al, 2019). Due to the variety of the landslide related parameters, it is not well clarified which combination of parameters would produce the best solution for a given landslide susceptibility problem.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Feature Selection for Landslide Susceptibility Mapping in Northern Iran

Nikraftar

Rajabi-Kiasari

Seydi

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Recognizing where landslides are most likely to occur is crucial for land use planning and decision-making especially in the mountainous areas. A significant portion of northern Iran (NI) is prone to landslides due to its climatology, geological and topographical characteristics. The main objective of this study is to produce landslide susceptibility maps in NI applying three machine learning algorithms such as K-nearest neighbors (KNN), Support Vector Machines (SVM) and Random Forest (RF). Out of the total number of 1334 landslides identified in the study area, 894 (≈67%) locations were used for the landslide susceptibility maps, while the remaining 440 (≈33%) cases were utilized for the model validation. 21 landslide triggering factors including topographical, hydrological, lithological and Land cover types were extracted from the spatial database using SAGA (System for Automated Geoscientific Analyses), ArcGIS software and satellite images. Furthermore, a genetic algorithm was employed to select the most important informative features. Then, landslide susceptibility was analyzed by assessing the environmental feasibility of influential factors. The obtained results indicate that the RF model with the overall accuracy (OA) of 90.01% depicted a better performance than SVM (OA=81.06%) and KNN (OA=83.05%) models. The produced susceptibility maps can be productively practical for upcoming land use planning in NI.

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Characterization and quantification of path dependency in landslide susceptibility

Cited by 82 publications

References 42 publications

Space-Time Landslide Predictive Modelling

Space-Time Landslide Predictive Modelling

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

Genetic Algorithm Based Feature Selection for Landslide Susceptibility Mapping in Northern Iran

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