Spatio-temporal landslide forecasting based on combination of rainfall thresholds and landslide susceptibility maps: a test in the Northern Apennines (Italy).

2020 ◽  
Author(s):  
Ascanio Rosi ◽  
Samuele Segoni ◽  
Veronica Tofani ◽  
Filippo Catani
Keyword(s):  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
End Users ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Coarse Spatial Resolution ◽  
Landslide Forecasting ◽  
Spatio Temporal ◽  
Landslide Susceptibility Maps

<p>Landslide forecasting and early warning at regional scale are difficult task and they are usually accomplished by the mean of statistical approaches aimed to define rainfall thresholds and landslide susceptibility maps.<br> Landslide susceptibility maps are based on the analysis of predisposing factors to assess the spatial probability of landslide occurrence, while rainfall thresholds are based on the correlation, valid on a wide area, between landslide occurrence and triggering factors, which usually are a couple of rainfall parameters, such as rainfall duration and intensity.<br>Susceptibility maps are static map that can be used for the spatial prediction of the most landslide prone areas, nut cannot be used to predict the temporal occurrence of a landslide triggering.<br>Rainfall thresholds can be used for temporal prediction, but with a coarse spatial resolution (usually some hundreds or thousands of km<sup>2</sup>), and the reference areas could contains both plains and hillslopes, so the alerts could involves both areas, even if landslides are improbable in river plains; this means that rainfall thresholds are not very suitable to identify the most probable triggering sites.<br>Rainfall thresholds and susceptibility maps can be therefore conveniently combined into dynamic hazard matrixes to obtain spatio-temporal forecasts of landslide hazard.<br>To combine these inputs, they are combined in a purposely-built hazard matrix, where each parameter is classified into 3 classes: landslide susceptibility map has been classified in S1 (low susceptibility), S2 (medium susceptibility) and S3 (high susceptibility), while rainfall rate has been classified in the classes R1, R2 and R3, by the definition of 2 rainfall thresholds.<br>The combination of the aforementioned classes allowed to define a matrix with 5 hazard classes, from H0 (null hazard) to H4 (high hazard), which was calibrated so that there was not any landslide in the H0 class and that the 90% of the landslide were in H2-H4 classes.<br>The result of this procedure is a dynamic hazard map, where the hazard, which is calculated for each pixel, can change over the time, based on rainfall rate variations.<br>For operational purposes, such a map cannot be used, since the pixel based resolution is too fine to be used during an emergency or to plan any activity in the planification phase, so the results have been aggregated at municipality scale, which is more easily readable for the end-users as local administrators and decision makers.<br>In this way it is possible to overcome the issues due to the stillness of susceptibility maps and to the coarse spatial resolution of rainfall thresholds, also avoiding results which could be hardly understandable outside of the scientific community.<br>This procedure was tested in a test site located in Northern Tuscany (Italy) and the work showed the possibility of obtaining results which are balanced between the scientific soundness and the needs of end-users like mayors, local administrators and civil protection personnel.</p><p> </p>

Integrating landslide susceptibility maps into warning models at regional scale in Italy

2021 ◽  
Author(s):  
Gaetano Pecoraro ◽  
Michele Calvello
Keyword(s):  
Spatial Resolution ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Regional Scale ◽  
Susceptibility Map ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

<p>The importance of susceptibility maps in the initial phase of landslide hazard and risk assessment is widely recognized in the literature, since they provide to stakeholders a general overview of the location of landslide prone areas. Usually, the use of these maps is limited to support land use planning. However, many researchers have recently recognized that susceptibility maps may also be used to improve the performance and spatial resolution of landslide warning at regional scale and provide a better updating of hazard assessment over time. Indeed, landslides prediction may be difficult at regional scale only considering rainfall condition, due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geology, and lithology). As a result, a critical issue of models solely based on rainfall thresholds may be the issuing of warnings in areas that are not prone to landslide occurrence, resulting in an excessive number of false positives. In this work, we propose a methodology aimed at combining a susceptibility map and a set of rainfall thresholds by using a matrix approach to refine the performance of an early warning model at regional scale. The main aim is the combination of rainfall thresholds (typically used to accomplish a dynamic temporal forecasting with good temporal resolution but very coarse spatial resolution), with landslide susceptibility maps (providing static spatial information about the probability of landslide occurrence with a finer resolution). The methodology presented herein could allow a better prediction of “where” and “when” landslides may occur, thus: i) allowing to define a time-dependent level of hazard associated to their possible occurrence, and ii) markedly refining the spatial resolution of warning models employed at regional scale, given that areas susceptible to landslides typically represent only a fraction of territorial warning zones.</p>

scholarly journals Assessing the quality of landslide susceptibility maps – case study Lower Austria

2013 ◽  
Vol 1 (2) ◽  
pp. 1001-1050 ◽  
Author(s):  
H. Petschko ◽  
A. Brenning ◽  
R. Bell ◽  
J. Goetz ◽  
T. Glade
Keyword(s):  
Confidence Intervals ◽  
Landslide Susceptibility ◽  
Spatial Planning ◽  
Cross Validation ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
High Interest ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Landslide susceptibility maps are helpful tools to identify areas which might be prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, the quality of the landslide susceptibility map and of the model applied to compute them is of high interest. In this study we focus on the analysis of the model performance by a repeated k-fold cross-validation with spatial and random subsampling. Furthermore, the focus is on the analysis of the implications of uncertainties expressed by confidence intervals of model predictions. The cross-validation performance assessments reflects the variability of performance estimates compared to single hold-out validation approaches that produce only a single estimate. The analysis of the confidence intervals shows that in 85% of the study area, the 95% confidence limits fall within the same susceptibility class. However, there are cases where confidence intervals overlap with all classes from the lowest to the highest class of susceptibility to landsliding. Locations whose confidence intervals intersect with more than one susceptibility class are of high interest because this uncertainty may affect spatial planning processes that are based on the susceptibility level.

scholarly journals Assessing the quality of landslide susceptibility maps – case study Lower Austria

2014 ◽  
Vol 14 (1) ◽  
pp. 95-118 ◽  
Author(s):  
H. Petschko ◽  
A. Brenning ◽  
R. Bell ◽  
J. Goetz ◽  
T. Glade
Keyword(s):  
Landslide Susceptibility ◽  
Additive Model ◽  
Susceptibility Map ◽  
Model Quality ◽  
Open Communication ◽  
Entire Study ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, several aspects of the quality of the landslide susceptibility model and the resulting classified map are of high interest. In this study of landslides in Lower Austria, we focus on the model form uncertainty to assess the quality of a flexible statistical modelling technique, the generalized additive model (GAM). The study area (15 850 km2) is divided into 16 modelling domains based on lithology classes. A model representing the entire study area is constructed by combining these models. The performances of the models are assessed using repeated k-fold cross-validation with spatial and random subsampling. This reflects the variability of performance estimates arising from sampling variation. Measures of spatial transferability and thematic consistency are applied to empirically assess model quality. We also analyse and visualize the implications of spatially varying prediction uncertainties regarding the susceptibility map classes by taking into account the confidence intervals of model predictions. The 95% confidence limits fall within the same susceptibility class in 85% of the study area. Overall, this study contributes to advancing open communication and assessment of model quality related to statistical landslide susceptibility models.

scholarly journals Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

2018 ◽  
Vol 10 (8) ◽  
pp. 1252 ◽  
Author(s):  
Prima Kadavi ◽  
Chang-Wook Lee ◽  
Saro Lee
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Learning Models ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Multiclass Classifier ◽  
Landslide Susceptibility Maps ◽  
Machine Learning Models

The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods.

DEVELOPMENT OF LANDSLIDE SUSCEPTIBILITY MAPS IN COLORADO

2016 ◽  
Author(s):  
Kassandra Lindsey ◽  
◽  
Matthew L. Morgan ◽  
Karen A. Berry
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

scholarly journals A susceptibility-based rainfall threshold approach for landslide occurrence

2019 ◽  
Vol 19 (4) ◽  
pp. 775-789 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin
Keyword(s):  
Landslide Susceptibility ◽  
Rainfall Threshold ◽  
Exceedance Probability ◽  
Equal Weight ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Continental Scale ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

Influence of DEM spatial resolution on the susceptibility mapping with SHALSTAB in the Rio Garcia hydrographic basin, municipality of Blumenau/SC / Influência de la resolución espacial del MDE en la cartografía de susceptibilidad con SHALSTAB en la cuenca hidrográfica del Río García, municipio de Blumenau/SC

2021 ◽  
Vol 5 (3) ◽  
pp. 1475-1491
Author(s):  
Gisele Marilha Pereira Reginatto ◽  
Regiane Mara Sbroglia ◽  
Camilo Andrade Carreño ◽  
Bianca Rodrigues Schvartz ◽  
Pâmela Betiatto ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Hydrographic Basin ◽  
Susceptibility Maps ◽  
Digital Elevation ◽  
Stability Model ◽  
Elevation Model ◽  
Del Rio

In translational landslide susceptibility analysis with SHALSTAB (Shallow Landsliding Stability Model), the resolution of the digital elevation model (DSM) is determinant for defining the type of mapping generated (preliminary or not). In this study, in order to verify the influence of the SDM scale on the SHALSTAB stability classes, susceptibility maps were prepared at two scales: 1:50,000 and 1:10,000. The study area was the Garcia River watershed, belonging to the municipality of Blumenau, Santa Catarina, affected by landslides in the 2008 catastrophe, which enabled the validation of the simulations with the scars mapped in the field. Thus, the influence of scale on the distribution of the model's stability classes and on its performance was verified. SHALSTAB performed better at the 1:10,000 scale, predicting 70% of the instabilities in a percentage of unstable area approximately three times smaller than at the 1,50,000 scale.

scholarly journals Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

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