LAND-SE : a software for statistically-based landslide 1 susceptibility zonation , Version 1 . 0 2 3 (original) (raw)
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LAND-SE: a software for landslide statistically-based susceptibility zonation, Version 1.0
Geoscientific Model Development Discussions, 2016
Landslide susceptibility (LS) provides an estimate of the landslide spatial occurrence based on local terrain conditions. LS has been evaluated in many locations around the world since the early '80 using distinct modelling approaches, diverse combination of variables, and different partition of the territory (mapping units). Among the different methods, statistical models have been largely used to assess LS and several model types have been proposed in the literature. A recent literature review revealed that authors not always present a complete and comprehensive assessment of the LS that includes model performance analysis, prediction skills evaluation and estimation of the errors and uncertainty. <br><br> The aim of this paper is to describe LAND-SE (LANDslide Susceptibility Evaluation), software that performs susceptibility modelling and zonation using statistical models, quantifies the model performances and the associated uncertainty. The software is implemente...
Springer eBooks, 2015
Landslide susceptibility maps can be elaborated using a variety of methodological approaches. This study investigates quantitative and qualitative differences between two statistical modelling methods, taking into account the impact of two different response variables (landslide inventories) for the Rhenodanubian Flysch zone of Lower Austria. Quantitative validation of the four generated susceptibility maps is conducted by calculating conventional accuracy statistics for an independent random landslide subsample. Qualitative geomorphic plausibility is estimated by comparing the final susceptibility maps with hillshades of a high resolution Airborne Laser Scan Digital Terrain Model (ALS-DTM). Spatial variations between the final susceptibility maps are displayed by difference maps and their densities. Although statistical quality criterions reveal similar qualities for all maps, difference maps and geomorphic plausibility expose considerable differences between the maps. Given that, this conclusion could only be drawn by evaluating additionally the geomorphic plausibility and difference maps. Therefore, we indicate that conventional statistical quality assessment should be combined with qualitative validation of the maps.
Preliminary regional landslide susceptibility assessment using limited data
Geologia Croatica, 2019
In this paper a heuristic approach for preliminary regional landslide susceptibility assessment using limited amount of data is presented. It is called arbitrary polynomial method and takes into account 5 landslide conditioning parameters: lithology, slope inclination, average annual rainfall, land use and maximum expected seismic intensity. According to the method, in the first stage, a gradation is performed for each of the carefully selected conditioning parameters by assigning so called rating points to the grid cells on which the region is divided. Values of the rating points vary from 0 to 3 and depend on the parameter's character and importance for landslide development within the region of interest. A so called Total Landslide Susceptibility Rating (TLSR) model is obtained by summing the individual rating points of each parameter and dividing the region into five susceptibility zones according to Jenks natural breaks classification. Verification of the TLSR model is then performed by overlaying the landslide inventory map of the selected region over the prepared susceptibility map. The sensitivity of the model can be additionally tested by multiplying the conditioning parameter's rating points by sensitivity coefficients. In this way, additional landslide susceptibility models are obtained, named Weighted Total Landslide Susceptibility Rating (WTLSR) models. As a practical example of the method, two TLSR models are presented here for the Polog region in Republic of Macedonia, for return periods of maximum expected seismic intensity for 100 and 500 years. With over 74% of mapped landslides falling in zones of high and very high susceptibility, the results are considered satisfactory for regional scale landslide modelling and are comparable with more advanced quantitative methods. Additional WTLSR models were prepared, and their correlation identified the best model. The presented approach is considered to be very convenient for conducting preliminary regional landslide susceptibility assessments with the ability to fine-tune the results. Due to its simplicity, it can be applied to additional landslide conditioning parameters other than the one presented in the paper, depending on the region of interest and available data sources. It is especially practical for use in developing countries, where various organizational, technical and economic constraints prevent application of more advanced data driven methods. Limitations and restrictions of the approach are also discussed.
LAND-SUITE V1.0: a suite of tools for statistically-based landslide susceptibility zonation
2021
In the past 50 years, a large variety of statistically-based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations and several approaches to evaluate the models performance. Despite the 10 numerous applications available in the literature, a standard approach for susceptibility modelling and zonation is still missing. The literature search revealed that several articles describe tools that apply physically based models for susceptibility zonation, but only few use statistically-based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models, and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically-based 15 landslide susceptibility modelling which integrate, extend and complete LAND-SE. LAND-SUITE is able to: i) facilitate input data preparation; ii) perform preliminary and exploratory analysis of the available data; iii) test different combinations of variables and select the optimal thematic/explanatory set; iv) test different model types and their combinations; and v) evaluate the models performance and uncertainty. LAND-SUITE provides a tool that can assist the user to reduce some common source of errors coming from the data preparatory phase, and to perform more easily, more flexible and more informed statistically-20 based landslide susceptibility applications.
gisc.gr
Landslides are considered among the most important geohazards as they cause substantial losses worldwide. Landslide susceptibility assessment (LSA) is related to certain influencing factors. Statistical modelling is one of the most preferred methods for LSA and mapping. Although landslide occurrences and influencing factors have spatial variations, global models ignore spatial dependence or autocorrelation characteristics of data in susceptibility assessment. In this paper the use of global and local LSA methods is examined. Thus, geographical weighted regression (GWR) is compared to standard logistic regression (LR) approach. The proposed models were implemented to Peloponnese peninsula in south Greece. Topographic (morphometric) parameters (slope angle, elevation), geological parameters and other parameters (landcover, rainfalls), were considered as landslide influencing factors. These influencing factors as well as the landslide events inventory in the study area were considered in order to obtain landslide susceptibility maps by using LR and GWR models. The results show the potential improvement in landslide susceptibility assessment by the use of GWR.
Assessing the quality of landslide susceptibility models - case study Lower Austria
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.
Zonation of Landslide Susceptibility in the Gipuzkoa Province (Spain): An Application of LAND-SUITE
2022
In the past 50 years, a large variety of statistically based models and methods for landslide susceptibility zonation have been proposed in the literature. The numerous methods, applicable to a large range of spatial scales, use several input thematic data, different model combinations and various approaches to evaluate the model performance. In the literature, only few articles describe tools that apply statistically based approaches for the susceptibility evaluation. This paper describes and illustrates, through an example in the Gipuzkoa province (Spain), the use of LAND-SUITE, a tool for the statistically based landslide susceptibility zonation. The application aims to show how LAND-SUITE provides utilities to: (i) support the user for the input data preparation; (ii) perform preliminary and exploratory analysis of the available data; (iii) test different combinations of variables and select the optimal thematic/explanatory set; (iv) test different model types and their combinations; and (v) evaluate the model performance and uncertainty. The suite showed high flexibility and allowed to perform different susceptibility applications, with diversified training/ validation datasets partitions and validation tests. Given its specifications, LAND-SUITE can be easily applied elsewhere to perform similar studies but also to explore other landslide susceptibility applications.
IRJET- A REVIEW OF LANDSLIDE SUSCEPTIBILITY ASSESSMENT MODELS
IRJET, 2021
Landslides are always a threat to human society, worldwide. Being able to accurately estimate landslide susceptibility spatially and temporally is foundational to the management of many landslide-prone areas around the world. Landslide occurrences are largely controlled by different and manifold causative factors. Topography, lithology, tectonics, rainfall, vegetation, and human activities all affect the natural stability of slopes and determine the susceptibility of a landscape to landslides. Therefore, characterizing the spatial patterns of landslide occurrences under natural geoenvironmental causatives factors over the large-scale landscape is an extremely difficult task with field surveys alone. This paper reviews three landslide susceptibility assessment methods viz-a-viz, Multinomial Logistic Regression Model (MLR), Deep Learning Model (DL) and Logistic Regression Model (EBF-LR). As a replacement for field methods, modelling the landslide susceptibility is an attractive alternative that can provide analytic frameworks for quantifying and understanding the underlying patterns of this phenomenon under various local conditions.
Retrospective evaluation of landslide susceptibility maps and review of validation practice
Environmental Earth Sciences, 2021
Despite the widespread application of landslide susceptibility analyses, there is hardly any information about whether or not the occurrence of recent landslide events was correctly predicted by the relevant susceptibility maps. Hence, the objective of this study is to evaluate four landslide susceptibility maps retrospectively in a landslide-prone area of the Swabian Alb (Germany). The predictive performance of each susceptibility map is evaluated based on a landslide event triggered by heavy rainfalls in the year 2013. The retrospective evaluation revealed significant variations in the predictive accuracy of the analyzed studies. Both completely erroneous as well as very precise predictions were observed. These differences are less attributed to the applied statistical method and more to the quality and comprehensiveness of the used input data. Furthermore, a literature review of 50 peer-reviewed articles showed that most landslide susceptibility analyses achieve very high validat...
Susceptibility Maps for Landslides Using Different Modelling Approaches
Springer eBooks, 2013
This study focuses on the comparison of different modelling approaches and is part of the research project "MoNOE" (Method development for landslide susceptibility modelling in Lower Austria). The main objective of the project is to design a method for landslide susceptibility modelling for a large study area. For other objectives of the project see Bell at al. in this volume. To reach the main objective two different statistical models, Weights of Evidence and Logistic Regression are applied and compared. By using nearly the same input data in test areas it is possible to compare the capabilities of these different methods. First results of the comparison indicate that in valleys and on south directed slopes the results of the two different modelling approaches are quite similar. The results on north directed slopes show differences. In the ongoing work the reason for these differences will be analysed. Also it will be necessary to find adequate validation methods for the two modelling approaches.