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Probabilistic prediction models for landslide hazard mapping
Abstract and Figures
A joint conditional probability model is proposed to represent a measure of a future landslide hazard, and five estimation procedures for the model are presented. The distribution of past landslides was divided into two groups with respect to a fixed time. A training set consisting of the earlier landslides and the geographical information system-based multi-layer spatial data in the study area was used to construct the prediction maps. The predictions were then cross-validated by comparing them with the remaining later landslides. When the database falls short of providing sufficient support for the prediction, the model allows the introduction of the expert's knowledge to modify the observed frequencies of the landslides with respect to the spatial data. The additional information should improve the prediction results. A case study from the Rio Chincina region in Colombia was used to illustrate the methodologies.
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... Namely, for each landslide susceptibility model, a curve is plotted by defining cumulative landslide area against cumulative susceptibility area with a 0.01 interval of probabilistic value to calculate AUC. As defined in Fabbri 1999 andFabbri 2003, this is used to measure success and prediction rate (e.g. in bivariate analysis Moazzam et al. 2020 andSinčić et al. 2022b) by examining training and validation landslides, respectively. In our case, all landslides will be examined to emphasize applicability on a local scale, i.e. measuring classification capabilities of the models for all mapped landslides only. ...
... In fact, the inventory of past occurrences remains the most influential input factor on the calculation of the spatial probability of landslide occurrence (Pereira et al. 2012;Bounab et al. 2022), which makes the landslides mapping a decisive step. Landslide inventory maps are prepared for several objectives, such as defining the location and type of landslides in the study area (Antonini et al. 2002;Cardinali et al. 2001), showing the abundance of slope movements (Degraff 1985;F Guzzetti et al. 2000), determining the frequency-area statistics of slope failures (Hovius et al. 1997(Hovius et al. , 2000Guzzetti et al. 2002;Malamud et al. 2004), and providing relevant information to construct landslide susceptibility and hazard models (Soeters and Van Westen 1996;Chung and Fabbri 1999, 2003, 2005Guzzetti et al. 2005Guzzetti et al. , 2006. ...
Slope movements are dangerous geohazards causing serious socio-economic damages on unstable slopes. In the last decade, the number of landslides research studies has increased rapidly because of their complexity, involving multiple parameters varying in time and space, their great potential to hinder the socio-economic development and especially the high budgets invested in risk mitigation interventions worldwide. In active mountain belts as the Pre-Rif unit, both conditioning and triggering factors are present and human activity is often involved either through land use favouring instability, or the disturbance of hillslopes, and without omitting the vulnerability presented by certain urban and peri-urban extensions. Nowadays, hazard mapping and its integration into approved land-use planning documents is one of the preliminary and most effective means of mitigating and managing natural hazards. Hence, the public authorities have launched several tenders aimed at producing maps of suitability for urbanization (CAU), particularly regarding the risk of slope movements, in several of the kingdom's provinces. Within the framework of these projects, the present work has been carried out. Numerous approaches are used in landslide studies, heuristic, deterministic and statistical depending on the geomorphic context, scale, data availability and especially the objectives targeted. In the present research work, three types of approaches are elaborated to investigate landslide hazard in the Fez-Moulay Yacoub region. The deterministic methods developed have proved their effectiveness and complementarity in the study of this hazard in densely urbanized areas and at the scale of detail, providing precise information on the extent and kinematics of landslides affecting the urban center of Moulay Yacoub. As for the heuristic methods, the mapping of the susceptibility to ground movements at a broad scale gave results of high quality and of crucial utility. the analysis and evaluation of the conditioning parameters revealed that the anthropogenic factors are strongly involved, notably the use of land and the proximity to the road network, in addition to the classic factors of predisposition (slope, proximity to the hydro network, etc.).
Several statistical methods have been used in this work to investigate the impact of
topographic growth conditioned by active tectonics on the magnitude of ground movements in the southern Riffian front. The results showed the difference in terms of typology and slope dynamics between the southern edge of the Prerif and the hilly landscape dominating the province of Moulay Yacoub. Finally, the analysis of the impact of landslides carried out on several urban extensions showed that human activity is strongly involved in the instability of the slopes, especially because it presents a high vulnerability. Moreover, among the areas investigated, the urban center of Moulay Yacoub as well as the urbanized outskirts of the city of fez proved to be the most vulnerable to slope movements and highly exposed.
... Geological hazard susceptibility evaluation is an important link and basis for disaster prevention and reduction (Chen et al., 2005;Ma et al., 2021). Currently, the commonly used susceptibility evaluation models include analytic hierarchy process model (Chung and Fabbri, 1999;Wang et al., 2009;Xu et al., 2009), weighted information value model (Wang et al., 2014;Jiao et al., 2019;Alsabhan et al., 2022), logistic regression model (Budimir et al., 2015;Tang and Ma, 2015), artificial neural network model (Nourani et al., 2014), support vector machine model (Kavzoglu et al., 2014), etc., among which the weighted information value model is widely used in the research field of geological disaster susceptibility evaluation due to its clear physical significance and simple algorithm. Shen H et al. (Shen et al., 2021) established a weighted information value model based on the weight value and information quantity value of each index obtained by the analytic hierarchy process (AHP) and information value model to conduct a comprehensive assessment of landslide susceptibility. ...
Given the inconsistency between the information value and the weight value in the weighted information value model, a weight model based on the Apriori algorithm is established in this paper to analyze the correlation between the second-level intervals of disaster factors and the susceptibility of geological disasters. The objective weight of the second-level intervals of each index factor is calculated through the mining of association rules by the Apriori algorithm. The subjective uncertainty of the existing second-level factor weighting method is eliminated. Taking the geological disaster data of Xiangtan urban area as an example, 10 evaluation indexes were selected to establish the entropy weight method-information value (EWM-IV) model and the entropy weight method-Apriori algorithm-information value (EWM-Apriori-IV) model to evaluate the geological disaster susceptibility, and the disaster area ratio and the receiver operating characteristic curve (ROC) verification method were used to test and analyze the evaluation results. The results showed that compared with the EWM-IV model, the EWM-Apriori-IV model is used to evaluate the disaster area ratio of high-prone area increased by 58.3%, and the disaster area ratio of low-prone area decreased by 43.1%, the area under the curve (AUC) increased by 7.4%, and the evaluation accuracy was relatively improved compared with the former. This paper proves the rationality and practicability of the weighting method of the geological hazard susceptibility evaluation index based on the Apriori algorithm.
... The accumulative area percentage with respect to 50 bins (as the horizontal coordinate) and their corresponding accumulative area percentage of coseismic landslide samples (as the vertical coordinate) were calculated. These values of the accumulative area percentage were used to draw the desired ROC curve and calculate the AUC (Figure 14) (Chung and Fabbri, 1999). With the increase in the accumulative area percentage, the corresponding accumulative area percentage of coseismic landslides increased rapidly at a faster rate, then increased slowly, and finally reached 100%. ...
Seismic landslide hazard assessment plays a very important guiding role during urgent earthquake relief. In August 2017, an Ms 7.0 earthquake in Jiuzhaigou County, Sichuan Province, China, triggered thousands of landslides. Based on the analysis of geological settings and coseismic landslide characteristics, the Newmark model is used to complete the seismic landslide hazard assessment. Three seismic motion parameters, namely, peak ground acceleration (PGA), traditional Arias intensity (Arias_P), and improved Arias intensity (Arias_C), are adopted. A publicly published coseismic landslide catalog is used as the validation samples. The results show that the coseismic landslides are mainly distributed in the deep gullies and steep mountainous slopes on the north and south sides of the epicenter. The seismic landslide hazard accuracy based on Arias_C is the best, followed by that based on PGA and Arias_P. The spatial distribution of seismic landslide hazards based on Arias_C shows an almost standard elliptical ring and is in good agreement with that of coseismic landslides. These results fully reflect the combined influence of the epicenter and seismogenic fault on landslide development. The middle seismic landslide hazard and over are mainly located at areas with seismic intensity of VII degree and above. The Arias intensity is very suitable for rapid seismic landslide hazard assessment in emergency situations. The study results can provide scientific and technological support for rapid earthquake relief and have reference significance for future seismic landslide hazard assessment.
Background
The Yunnan section of the Nujiang River (YNR) Basin in the alpine-valley area is one of the most critical areas of debris flow in China.
Methods
We analyzed the applicability of three machine learning algorithms to model of susceptibility to debris flow—Random Forest (RF), the linear kernel support vector machine (Linear SVM), and the radial basis function support vector machine (RBFSVM)—and compared 20 factors to determine the dominant controlling in debris flow occurrence in the region.
Results
We found that (1) RF outperformed RBFSVM and Linear SVM in terms of accuracy, (2) topographic conditions were prerequisites, and geology, precipitation, vegetation, and anthropogenic influence were critical to forming debris flows. Also, the relative elevation difference was the most prominent evaluation factor of debris flow susceptibility, and (3) susceptibility maps based on RF’s debris flow susceptibility (DFS) showed that zones with very high susceptibility were distributed along the mainstream of the Nujiang River. These findings provide methodological guidance and reference for improvement of DFS assessment. It enriches the content of DFS studies in the alpine-valley areas.
Among the various natural geological phenomenon affecting the Indian peninsula, landslides have had huge damaging effects on human life and infrastructure, thereby requiring researchers to delve into more in-depth landslide-related studies with a focus on finding effective methods to better delineate landslide-prone areas as well as better understand the various underlying causative factors. The district of Kullu, situated in the Indian state of Himachal Pradesh has been the witness to some of these disastrous events, possibly owing to a recent surge in tourism, erratic climatic variation, and un-scientific construction. This study is an attempt to evaluate the applicability and effectiveness of four probabilistic approaches; Frequency Ratio (FR), Shannon Entropy (SE), Information Value (IV), and Weight-of-Evidence (WoE) in a Geographic Information System (GIS) environment for producing landslide susceptibility maps, to aid local authorities, in finding better-suited land use planning and risk-reduction strategies. The nine selected causative factors: slope, aspect, land use/land cover, elevation, curvature, distance to faults/lineaments, distance to roads, distance to drainage, and lithology along with an updated landslide inventory, compiled from past incidence maps from the Geological Survey of India (GSI) and through visual interpretation of google earth imageries (2001–2019) formed the input for this research work. Three different metrics namely, landslide density index (LDI), relative landslide density index (Rindex), and area under curve (AUC) were then used to validate and compare the resulting landslide susceptibility maps. The highest model fitness and predictive ability were demonstrated by the frequency ratio and shannon entropy approaches for this geographical extent.
The growing popularity of GIS technology in Ethiopia has encouraged multiple scholars to investigate landslide hazards using quantitative approaches, despite its limitations. The present review examined the approach used in the evaluation of landslide hazards by five prior studies that shared catchments. The review results reveal that the controlling factors assumed by the five researchers were inconsistent and resulted in highly divergent frequency ratio (FR) values, even for the same factors. This implies that the contribution of a single instability factor can be inferred sufficiently for landslide hazard assessment and mapping; otherwise, the results are highly subjective and disputable. Since the soil type in the region was alluvial-colluvial in the five studies, and a majority of the failures occurred shortly after rainfall, rainfall data and basic soil properties (classification and shear strength) should not be overlooked. In addition to the nonstandard use of morphometric parameters, the inherent limits of GIS methodologies, the omission of hydrogeotechnical properties, and the observed subjective outcomes make the GIS-based approach imprecise, error-prone, and doubtful. The total effect will result in ineffective early warning systems and unworthy mitigation measures, resulting in significant life costs and damage. As a result, it is recommended that GIS technology should be coupled with software (TRIGRS, Scoops3D, SINMAP, OpenLISEM, GLM, and SLIP) that considers hydrogeotechnical properties to provide more reliable conclusions. In addition to using instability factors consistently, regional statistical correlations of all morphometric parameters can be developed, allowing for less complex and realistic empirical models to be used.
The aim of this paper is to show the susceptibility to sliding in the Krupanj area. Intense rainfall in May 2014 triggered many landslides in western Serbia. The Krupanj area was particularly affected by this event. The material damage occurred affected the awareness of the importance of knowing the locations that are prone to sliding. Therefore, from 2014 to 2021, field research was carried out in the Krupanj area. During this period a large amount of landslide data were collected by engineering geological mapping. These data were used for susceptibility analysis. For present study the analytical hierarchy process (AHP) and weight overlay (WO) tool were used. Nine factors were used for susceptibility analysis: slope, aspect, curvature, elevation, lithology, distance from rivers, faults, boundary?s and land cover. In order to be able to make a comparison, all factors were evaluated using the Saaty scale, so that the weights of the individual factors were obtained. The weight values obtained by AHP were used in GIS tool. Final map was validated by ROC analysis. The validation results show accuracy of 77,1% (good) for model.
Landslide susceptibility assessment and prediction are among the main processing for disaster management and land use planning activities. Therefore, the general purpose of this research was to evaluate GIS-based spatial modeling of landslides in the western Algiers Province using five models, namely, frequency ratio (FR), weights of evidence (WoE), evidential belief function (EBF), logistic regression (LR), and analytical hierarchy process (AHP), and then compare their performances. At first, a landslide inventory map was prepared according to Google Earth satellite images, historical records, and extensive field surveys. The recorded landslides were divided into two groups (70% and 30%) to establish the training and validation models. In the next step, GIS techniques and remote sensing data were used, to prepare a spatial database containing 13 landslide conditioning factors: lithology, distance to lithological boundaries, permeability, slope, exposure, altitude, profile curvature, plan curvature, precipitation, distance to rivers, topographic wetness index, normalized difference vegetation index, and distance to roads. Finally, the landslide susceptibility maps were produced using the five models and validated by the areas under the relative operative characteristic curve (AUC). The AUC results showed a significant improvement in susceptibility map accuracy; the FR model has the best performance in the training and prediction process (90%), followed by LR (88%, 89%), WoE (88%, 87%), EBF (86%,86%), and AHP (76%,75%), respectively. The produced maps in the current study could be useful for land use planning and hazard mitigation purposes in western Algiers Province.
This PhD thesis deals with different approaches of using GIS for Landslide Hazard Assessment
Based on several layers of spatial map patterns, multivariate regression methods have been developed for the construction of landslide hazard maps. The method proposed in this paper assumes that future landslides can be predicted by the statistical relationships established between the past landslides and the spatial data set of map patterns. The application of multivariate regression techniques for delineating landslide hazard areas runs into two critical problems using GIS (geographic information systems): (i) the need to handle thematic data; and (ii) the sample unit for the observations. To overcome the first problem related to the thematic data, favourability function approaches or dummy variable techniques can be used.
This paper deals with the second problem related to the sample units. In this situation, the unique condition subareas are defined where each subarea contains a unique combination of the map patterns. Weighted least squares techniques are proposed for the zonation of landslide hazard using those unique condition subareas. The traditional pixel-based multivariate regression model becomes a special case of the proposed weighted regression model based on the unique condition subareas. This model can be directly applied to vector-based GIS data without the need of rasterization.
A case study from a region in central Colombia is used to illustrate the methodologies discussed in this paper. To evaluate the results adequately, it was pretended that the time of the study was the year 1960 and that all the spatial data available in 1960 were compiled including the distribution of the past landslides occurred prior to that year. The statistical analyses performed are based on these pre-1960 data about rapid debris avalanches. The prediction was then compared with the distribution of the landslides which occurred during the period 1960–1980.
Landslide identification and mapping are the fundamental steps in every attempt to assess landslide hazard and risk. Comparison of landslide maps produced by different investigators or through different techniques indicates the inherent uncertainty related to this task. As with other natural catastrophies, the prediction of landslide occurrence in space and time remains a problem that requires a renewed interdisciplinary effort. -from Authors
The geographic information system (GIS) has dramatically changed the rate at which georeferenced data can be produced, updated, and disseminated. It has made the production and analysis of geographic information more efficient, and has changed the way this information is perceived and used. GISes now play an essential role in the management of land resources from the local to the global scale, from municipal planning to natural resource assessment. Provides a concise and practical introduction to the use of GIS technology. General concepts are introduced with examples that show a wide range of applications including agriculture, land use planning, mining exploration, and municipal information management. A chapter is devoted to the fundamentals of remote sensing, which has become a major GIS data source. The procedures and equipment used for data input and output are reviewed. Data quality, data management, and GIS analysis functions are presented and, finally, the process of GIS implementation is examined. -after Author
An abstract is not available.
As part of a long-term project aimed at a better understanding of the geological and geomorphological factors that control slope instability phenomena in Southern Italy, multivariate models for assessing landslide incidence hazard were developed and tested in two Calabrian sample areas characterized by different geological-geomorphological conditions. Discriminant analysis, based on a large set of mappable geological and geomorphical variables, is able to discriminate rather successfully between stable and unstable areas or slope units. Multiple regression analysis has also proved to be a useful tool in predicting actual and potential landslide hazard. Consequently, geomathematical models may provide a feasible approach to environmental hazard assessment, particularly when applied within the framework of a wider multidisciplinary project for land evaluation and planning.
In mineral exploration, resource assessment, or natural hazard assessment, many layers of geoscience maps such as lithology, structure, geophysics, geochemistry, hydrology, slope stability, mineral deposits, and preprocessed remotely sensed data can be used as evidence to delineate potential areas for further investigation. Today's PC-based data base management systems, statistical packages, spreadsheets, image processing systems, and geographical information systems provide almost unlimited capabilities of manipulating data. Generally such manipulations make a strategic separation of spatial and nonspatial attributes, which are conveniently linked in relational data bases. The first step in integration procedures usually consists of studying the individual charateristics of map features and interrelationships, and then representing them in numerical form (statistics) for finding the areas of high potential (or impact).Data representation is a transformation of our experience of the real world into a computational domain. As such, it must comply with models and rules to provide us with useful information. Quantitative representation of spatially distributed map patterns or phenomena plays a pivotal role in integration because it also determines the types of combination rules applied to them.Three representation methods—probability measures, Dempster-Shafer belief functions, and membership functions in fuzzy sets—and their corresponding estimation procedures are presented here with analyses of the implications and of the assumptions that are required in each approach to thematic mapping. Difficulties associated with the construction of probability measures, belief functions, and membership functions are also discussed; alternative procedures to overcome these difficulties are proposed. These proposed techniques are illustrated by using a simple, artificially constructed data set.
This paper describes an inductive modelling procedure integrated with a geographical information system for analysis of pattern within spatial data. The aim of the modelling procedure is to predict the distribution within one data set by combining a number of other data sets. Data set combination is carried out using Bayes’ theorem. Inputs to the theorem, in the form of conditional probabilities, are derived from an inductive learning process in which attributes of the data set to be modelled are compared with attributes of a variety of predictor data sets. This process is carried out on random subsets of the data to generate error bounds on inputs for analysis of error propagation associated with the use of Bayes’ theorem to combine data sets in the GIS. The statistical significance of model inputs is calculated as part of the inductive learning process. Use of the modelling procedure is illustrated through the analysis of the winter habitat relationships of red deer in Grampian Region, north-east Scotland. The distribution of red deer in Deer Management Group areas in Gordon and in Kincardine and Deeside Districts is used to develop a model which predicts the distribution throughout Grampian Region; this is tested against red deer distribution in Moray District. Habitat data sets used for constructing the model are accumulated frost and altitude, obtained from maps, and land cover, derived from satellite imagery. Errors resulting from the use of Bayes’ theorem to combine data sets within the GIS and introduced in generalizing output from 50 m pixel to 1 km grid squares resolution are analysed and presented in a series of maps. This analysis of error trains is an integral part of the implemented analytical procedure and provides support to the interpretation of the results of modelling. Potential applications of the modelling procedure are discussed.
Catastrophic landslides are a severe environmental problem on the Loess Plateau of China. In order to establish which slopes are most at risk, their spatial incidence can be modelled using geographical information systems techniques together with a database describing the past landslides. Three modelling strategies, sieve mapping, weighting factors and log-linear modelling, are adopted and compared. It is suggested that the log-linear modelling approach is the most general and makes best use of the available information.