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Landslide hazard assessment: Summary review and new perspectives
Abstract
This paper deals with several aspects of the assessment of hazard and risk of landsliding. In recent years the interest in this topic has increased greatly and there are many technical papers dealing with this subject in the literature. This article presents a summary review and a classification of the main approaches that have been developed worldwide. The first step is the subdivision between qualitative and quantitative methods. The first group is mainly based on the site-specific experience of experts with the susceptibility/hazard determined directly in the field or by combining different index maps. The approaches of the second group are formally more rigorous. It is possible to distinguish between statistical analyses (bivariate or multivariate) and deterministic methods that involve the analysis of specific sites or slopes based on geo-engineering models. Such analyses can be deterministic or probabilistic. Among the quantitative methods discussed is the Neural Networks approach which has only recently been applied to engineering geology problems. Finally several considerations concerning the concept of acceptable risk and risk management are presented.
... Many approaches and models, both qualitative and quantitative, have been put forth for predicting the susceptibility of landslides. (Aleotti & Chowdhury, 1999); Dai and Lee 2002;Sadr et al. 2014). The technique of "landslide susceptibility mapping, or LSM, is helpful for anticipating and identifying landslide events. ...
... Numerous writers endeavoured to categorize the LHZ methodology. (Reichenbach et al., 2018) (Guzzetti et al. 1999;(Aleotti & Chowdhury, 1999);For landslide susceptibility assessment and prediction, one of the most essential semiquantitative techniques is the Analytical Hierarchy Process (AHP). Saaty (1977) initially proposed this approach in social science studies. ...
Mapping landslide susceptibility is crucial for defining high-risk zones and preventing property and human casualties. The Uttarakhand provision, which comes under the Himalayan region, has a high potential for landslide occurrence. A landslide susceptibility map was created using satellite imagery, in-depth field research, and aerial photos. The historical landslide inventory of the state's 14698 total landslides was randomly bifurcated into 70% (10289) for training purposes and 30% (4409) for data validation. Eleven landslide-causative factors (Slope, Aspect, Curvature, Topographic Position Index (TPI), Topographic Wetness Index (TWI), Geology, Normalized Difference Vegetation Index (NDVI), Distance to Road, Distance to Stream, Distance to Fault, and Rainfall) were selected for susceptibility assessment. The landslide susceptibility zonation was created using the Shannon Entropy (SE), Frequency Ratio (FR), and Analytical Hierarchy Process (AHP) techniques, along with the causative factors. The AHP method is effectively utilized in LSM to prioritize and weigh the importance of different causative factors contributing to landslide occurrence, while Shannon Entropy uses the method of discrete probability distribution to quantify the uncertainty or variability associated with different causative factors. The FR, AHP, and SE models were validated using the AUC curve, yielding 92%, 89%, and 81% success rates and predictive rates of 90%, 87%, and 77%, respectively. The FR model is most suitable, more efficient, and valuable for future planning in the study area.
... Wide-area landslide risk evaluations are typically conducted using landslide inventories and susceptibility maps. Both qualitative and quantitative methods have been used to create landslide susceptibility maps [14][15][16]. Differences in engineers' experiences reduce their ability to evaluate the phenomenon correctly. Regarding the potential of quantitative methods to evaluate large areas efficiently, efforts are being made to utilize neural networks and artificial intelligence, particularly convolutional neural networks, to analyze images and other data [17,18]. ...
... The effective selection of landslide condition parameters is important for numerical analysis. Using three of the main parameters (topography, geology, and environment) proposed in [15] as references, the following eight parameters were selected: slope angle, eigenvalue ratio, curvature, overground openness, underground openness, topographic wetness index, wavelet transform, and normalized difference vegetation index (NDVI). We used QGIS ver3.16 to visualize the numerical results of this study. ...
The objective of this study was to identify the locations of deep-seated gravitational slope deformations (DGSDs) and define the numerical characteristics of these deformations to contribute to the sustainable management of social infrastructure in the event of an increased disaster. The topographic features of the DGSDs were quantitatively characterized based on their surface morphologies. Topographic features indicative of gravitational deformation in pre-slide topographic maps, such as terminal cliff failures, irregular undulations, and gullies, suggest that progressive deformation occurred over a prolonged period. To track the gravitational deformation over time, we interpreted aerial photographs of DGSDs from 1948 and 2012 associated with deep-seated landslides on the Kii Peninsula induced by Typhoon Talas on 2–5 August 2011. Corresponding numerical analysis of the gravitational deformations using 1 m digital elevation models reveals that landslide areas exhibit eight characteristic influencing factors, demonstrating that characteristic morphologies exist in areas that eventually experience landslides. One such morphological feature is the existence of a gently sloping area in the upper section of the deep-seated landslide mass, which comprises a catchment basin without a corresponding valley or gully. These findings suggest that rainwater penetrates the ground, and degrades and deforms the rock within the landslide mass, causing the slope to fail after torrential rainfall. This study holds great significance for advancing sustainable infrastructure development and management and mitigating environmental changes.
... As one of the prevalent types of natural hazards in the world's terrestrial environments with slopes (Froude and Petley 2018), landslides cause a large number of injuries, deaths and socio-economic losses every year (Corominas et al. 2014). The quantification of landslide susceptibility has also becoming increasingly important (van Westen et al. 2006;Gariano and Guzzetti 2016), evolving from the earliest qualitative comments to quantitative analysis (Ayalew and Yamagishi 2005), it has always been a hotspot for research by geologists and scholars from all over the world (Aleotti and Chowdhury 1999). The quality of its assessment is inextricably linked to the selected indexes and model. ...
... These will also serve as reference parameters for the identification of further indicators. Commonly used models for landslide susceptibility assessment include expert system models such as Analytic Hierarchical Progress (Kayastha et al. 2013), Expert Scoring method (Aleotti and Chowdhury 1999); Mathematical and Statistical models such as the Information Value Model (Che et al. 2012), Entropy Weighting scheme (Devkota et al. 2013), Right of Proof (Regmi et al. 2014), Logical Regression (Kavzoglu et al. 2014), and Machine-Learning models (Devkota et al. 2013), such as Decision Tree (Guo et al. 2021), Random Forest (Catani et al. 2013), Neural Network (Yang et al. 2019) and support vector machines (SVMs) (Pradhan 2013;Huang and Zhao 2018). Each of these models has its strengths, as well as certain weaknesses (Dou et al. 2019). ...
China's permafrost regions are affected by global warming and the thawing of permafrost, and the occurrence of landslide disasters has become more and more frequent, which makes the evaluation of the susceptibility to geologic hazards in China's permafrost regions is an urgent work to be carried out. Most of the existing geohazard susceptibility models have fewer test cases in the permafrost regions. Twelve evaluation factors, such as altitude, slope, slope direction, land use, and lithology, were selected to draw landslide hazard susceptibility maps by using three commonly used landslide susceptibility assessment models, including the information value model, the frequency ratio model and the random forest model, which can be implemented in GIS, taking the Lesser Khingan Mountains area located in the eastern part of permafrost region of northeast China as the study area. The applicability of the above commonly used landslide susceptibility assessment models in the permafrost regions is carried out by fieldwork and comparing the results of and model simulation. The Random Forest Model was also used to assess the importance of the factors that were adopted and to judge the degree of their influence on landslide development. The results show that the Information Value Model has a better applicability in the permafrost region. However, due to factors such as climate warming and permafrost degradation, the accuracy of the prediction results obtained by applying the existing commonly used landslide susceptibility assessment models in permafrost regions are still in need to be improved. Finally, thawing and degradation of permafrost will play a non-negligible role in influencing the occurrence of landslides in permafrost regions.
... The development of methodologies for landslide susceptibility, hazard and risk assessment, dates back to the 70's of the XXI century (Nilsen et al., 1979), intensively continued and applied during the nineties (Aleotti & Chowdhury, 1999), and today become the "main tool" used in the combat against these natural disasters, primarily in spatial planning (AGS, 2007;Cascini, 2008;Fell et al. 2008;Anderson & Holcombe, 2013;Abolmasov, 2016). ...
... Rockfall has been defined by Verne's (1978) as the movement of fragments of blocks of rock along a vertical or sub vertical cliff, which occurs mainly as a consequence of the pre-sequence of intersecting discontinuities in rocks. The study of landslides has drawn worldwide attention mainly due to increasing awareness of the socio-economic impact of landslides, as well as the increasing pressure of the urbanization on the mountain environment (Aleotti and Chowdhury, 1999). In Nepal, Brunsden et al. (1975) is one of the first to develop a geomorphological map of a road corridor. ...
Landslides occurs every year during monsoon season in the Nepal Himalaya which result in significant loss of life and property. Two types of landslides generally occur in Nepal every year, rainfall induced and seismic induced. Rough topography, extreme elevation change, fluctuating climate conditions, intricate geological formations as well as settlement in the slope and lack of awareness about foundation geology are main causative factors of landslides. Large-scale landslides frequently occur in the Lesser and Higher Himalayas due to the steep mountain slopes and active geological conditions. In this respect a study was carried out to investigate the cause and mechanism of Sillo Landslide located in the Adarsha Gaun Palika in Doti District which lies geologically in the Lesser Himalaya of Far-western Nepal. A small Todbillo landslide is a part of Sillo landslide. Kinematic analysis of joint and geo-mechanical classification of rock are used to find out nature of failure of rock and characteristics and quality of rocks. Wedge failure occurred in poor rock of crown parts of landslide is the result of study. Results from the study can be used as the preliminary study for landslide hazard assessment, land use planning and awareness of local people.
... Landslides are a significant natural hazard that cause considerable number of fatalities and substantial loss to environment, habitat, and infrastructure globally (Aleotti and Chowdhury 1999;Petley 2012;Froude and Petley 2018). While landslides are prevalent in mountain regions and occur frequently in nature, their predictability still needs to be fully achieved. ...
The cost of a landslide early warning system (LEWS) is a significant hurdle in its installation and adoption in developing nations like India, which has one of the highest rates of landslide occurrences worldwide. Previous research has focused on using low-cost monitoring instruments, cost-effective data transmission-management technologies, open-source technology, etc., to reduce the cost of LEWS. A possible strategy for reducing the price of a LEWS is to restrict the area that needs to be monitored. The cost of LEWS directly relates to the size of the slide. For instance, a massive landslide requires additional sensors to monitor the entire slide. However, for large landslides where progressive failure is confined to a particular zone, we must concentrate monitoring efforts on these critical areas for the optimal allocation of resources. For such slides, the number of sensors required can be reduced by focusing the deployment of sensors on the most vulnerable areas, leading to a lower cost. Hence, the current study proposes a generalized method for identifying potential failure zone/areas in large or deep-seated landslides that need to be monitored. This method not only provides economization of the number of sensors required but also ensures that data collection is focused and relevant, potentially enhancing the quality of monitoring and the accuracy of predictive models. The proposed method integrates different geotechnical approaches such as field investigation, laboratory testing, back analysis, and multi-temporal stability analysis. The method was tested on a deep-seated Kotropi landslide Himachal Pradesh initiated in 2017 and continuously experiencing progressive failures. A multi-temporal stability analysis was conducted in two phases. The first phase utilizes data collected during the 2018 field visit and estimates the probability of failure in different areas of the landslide. A field visit successfully validated the failure zone identified in the first phase. Furthermore, the second phase stability analysis, based on the data collected during the 2022 field visit, was performed to determine the future probability of failure in different slide areas. The in-depth analysis indicates that the Kotropi landslide is experiencing progressive failure, which limited to a particular zone in N and NW direction in contrast to the initial failure in NE-SW direction. Hence, using the proposed method, the area of a large landslide that needs to be monitored can be reduced by identifying the most vulnerable area, lowering the overall cost of a LEWS.
An evaluation of the areas susceptible to the occurrence of landslides in the coastal zone and representative hydrographic basins of the Guamá municipality was carried out. The Mora-Vahrson method was used through a Geographic Information System, with modifications to the original proposal according to the characteristics of the area. The susceptibility map was obtained, categorized into four levels: Low, Moderate, High and Very High. As a result of the zoning, it was obtained that high susceptibility is the most representative (52%); Towns such as El Macho, La Plata, Uvero, Montompolo and El Francés are located there. The result constitutes an update of the danger for the Guamá municipality and creates the bases for the development of local strategies for management and mitigation of impacts.
Across a gradient belt of the Western Sichuan Plateau, geohazards have seriously limited economic and social development. According to incomplete statistics, 15,673 geohazards have been recorded in the study area. In order to mitigate the threat of geohazards to human engineering activities in the region, an overall understanding of the distribution pattern of geohazards and susceptibility assessment are necessary. In this paper, a gradient belt of the Western Sichuan Plateau and its zoning criteria were defined. Subsequently, on the basis of relief amplitude, distance to faults, rainfall, and human activities, three indicators of endogenic process were introduced: Bouguer gravity anomaly gradient, vertical deformation gradient, and horizontal deformation gradient. Thereafter, the distribution patterns of geohazards were investigated through mathematical statistics and ArcGIS software. By randomly selecting 10,449 hazards, a geohazard susceptibility map was generated using the Information Value (IV) model. Finally, the IV model was validated against 5224 hazards using the Area Under Curve (AUC) method. The results show that 47.6% of the geohazards were distributed in the zone of steep slope. Geohazards showed strong responses to distance to faults, human activities, and annual rainfall. The distribution of geohazards in the gradient belt of the Western Sichuan Plateau is more sensitive to vertical internal dynamics factors (such as vertical deformation gradient and Bouguer gravity anomaly gradient) without any apparent sensitivity to horizontal internal dynamics factors. The areas of high and very-high risk account for up to 32.22%, mainly distributed in the Longmenshan and Anning River faults. According to the AUC plot, the success rate of the IV model for generating the susceptibility map is 76%. This susceptibility map and geohazard distribution pattern can provide a reference for geological disaster monitoring, preparation of post-disaster emergency measures, and town planning.
Landslides pose significant impact on human life and society such as loss of livelihood, destruction of infrastructure, and damage to natural resources around the world. Due to existing complications in conditional factors of landslide, mapping and predicting landslide occurrences with high accuracy needs more attention. In light of this, we aim to develop an ensemble landslide susceptibility model named as support vector regression–grasshopper optimization algorithm (SVR–GOA). This model is validated along with other landslide susceptibility models such as artificial neural network (ANN), boosted regression tree (BRT), and elastic net models. The present study carried out over the Kalaleh Basin in Iran, in which we selected 140 landslides with 16 conditional factors to construct a geographic database of the region. The multicollinearity analysis was done on the hazard conditioning factors using variance inflation factor and tolerance indices. Similarly, significance of these factors and their association with selected locations were identified through random forest method. The state of the art of the study is implementing SVR-GOA in landslide susceptibility mapping, including this model we use other landslide models such as ANN, BRT, and elastic net for validation and development using the area under the curve (AUC), kappa, and root mean squared error values. Our results show lithology, slope degree, rainfall, topography position index, topography wetness index, surface area, and landuse/landcover were found to be the most influential conditioning factors. We have also observed that, despite accurate prediction, SVR-GOA outperforms the others by showing the highest AUC values around AUC = 0.930 and others show ANN (AUC = 0.833), BRT (AUC = 0.822) and elastic net (AUC = 0.726) respectively. This innovative approach to landslide mapping using SVR-GOA ensembles would enhance the advancement of landslide research at multiple scales.
A presente pesquisa tem como objetivo avaliar o desempenho da aplicação da abordagem estatística de análise bivariada denominado Índice Estatístico (IE) integrada à análise multicritério denominada Analytic Hierarchy Process (AHP), em uma área de expansão urbana no município do Ipojuca, Pernambuco, Brasil, na escala 1:10.000. A pesquisa teve como base para elaboração e adaptação dos dados temáticos à referida escala, as ortofotocartas na escala 1:1.000 e o Modelo Digital do Terreno (MDT), os quais foram gerados, respectivamente, por levantamento aerofotogramétrico e LIDAR (Light Detection and Ranging). Verificou-se que na área piloto prevalecem os deslizamentos com superfície de ruptura translacional paralela ao talude, comumente deflagrados pela ação da água das chuvas e/ou águas servidas. Assim, foram utilizados no modelo os seguintes fatores condicionantes: litologia, solos, uso e cobertura da terra, declividade e curvatura de vertentes, os quais foram cruzados com o inventário de deslizamentos com representação pontual localizado na superfície de ruptura. A unidade espacial foi a célula de grade (pixel). Utilizou-se o IE para determinar o peso das classes de cada fator condicionante, e os índices básicos Accountability e Reliability, assim como, os pesos dos fatores (Wf), gerados com base nos resultados do IE, forneceram o grau de contribuição de cada fator condicionante nos deslizamentos e subsidiaram o preenchimento da matriz de comparação pareada da AHP. Constatou-se que o uso e cobertura da terra é mais determinante na ocorrência dos deslizamentos, acompanhado da declividade, solos, curvatura e litologia. O modelo gerado a partir da integração entre o Índice Estatístico e AHP apresentou um excelente desempenho com uma AAC=0,931 (93%) e uma excelente performance com uma AAC=0,906 (90%) para a avaliação da suscetibilidade a deslizamento no município do Ipojuca.
An example of geologic and landslide map of Pisticci surroundings has been made. The mass movements are slumps, slides as a renewal of the 'palaeo-landslides' movements, earthflow, creep and badland erosion. The large landslide of 1688, which destroyed a great part of Pisticci village, produced also some long fissures on its south edge, in which large landslides continue to occur. - from English summary
Spatial landslide hazard is determined in a forested area (French Alps) with steep slopes, consisting of gray marls, covered with morainic material. The cumulative probability that the F-value (safety factor) is lower than unity is calculated for different slope classes. The probability expressed as a percentage is compared with the spatial frequency of the number of landslides observed in the field. -Authors
The main requirements for assessing the system reliability of slopes are outlined briefly and a comprehensive procedure is presented based on potential slip surfaces of arbitrary shape. The evaluation of system reliability of soil structures such as slopes is important because of the spatial variability of geotechnical parameters within any soil layer and also the fact that slopes are frequently associated with multi-layered soil deposits. The performance function for the stability of a slope, defined in terms of a `factor of safety', is usually inexplicit and non-linear. A `rigorous' method of slope stability analysis such as the Generalized Procedure of Slices may be used as the basis of a reliability analysis. A complete analysis should include the search for a critical slip surface and the calculation of reliability index or the probability of failure associated with it. System reliability bounds are evaluated by considering the probabilities of failure associated with a number of slip surfaces including the critical slip surface. It is shown that the upper bound of the system failure probability can be significantly higher than the failure probability associated with the critical slip surface. This finding is significant for slope design and for geotechnical risk assessment generally.
