Landslide Disaster Risk Research Articles

Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras

Quantitative disaster risk studies for slow-moving rotational and translational landslides in small regions (e.g., cities and watersheds) are very scarce. The limitations of risk modeling associated with these hazards include (i) the lack of data for physical modeling, (ii) methodological restrictions on estimating landslide intensity with statistical models and determining the temporal probability of landslides, and (iii) the absence of characterizations of the physical vulnerability of exposed assets. The present study combines and updates different methodologies to overcome these limitations for quantitative landslide disaster risk estimation, creating a novel methodological approach that was applied in a pilot study in Tegucigalpa city, Honduras. Tegucigalpa, the capital city of Honduras, has the highest number of recorded landslides in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. As an input for the disaster risk assessment, this study generated landslide risk vulnerability functions based on empirical data. The application of the proposed methodology allowed us to estimate the average annual loss (AAL) caused by landslides in the study area—a key disaster risk metric that is lacking in other landslide disaster risk studies—enabling comparisons with disaster risk estimates associated with other hazards. In particular, the AAL value obtained for the study region was USD 7.26 million.

Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data

This study presents a method for classifying landslide triggers and sizes using climate and geospatial data. The landslide data were sourced from the Global Landslide Catalog (GLC), which identifies rainfall-triggered landslide events globally, regardless of size, impact, or location. Compiled from 2007 to 2018 at NASA Goddard Space Flight Center, the GLC includes various mass movements triggered by rainfall and other events. Climatic data for the 10 years preceding each landslide event, including variables such as rainfall amounts, humidity, pressure, and temperature, were integrated with the landslide data. This dataset was then used to classify landslide triggers and sizes using deep neural networks (DNNs) optimized through genetic algorithm (GA)-driven hyperparameter tuning. The optimized DNN models achieved accuracies of 0.67 and 0.82, respectively, in multiclass classification tasks. This research demonstrates the effectiveness of GA to enhance landslide disaster risk management.

Leveraging local knowledge for landslide disaster risk reduction in an urban informal settlement in Manado, Indonesia

Disaster preparedness and response, including for landslides, increasingly involves local knowledge. Incorporating contextual, dynamic and experience-based local knowledge leads to greater awareness of the interconnectedness of geological, natural, and social processes. Still, technical literature on urban landslide risk is mainly based on geological and natural dynamics and, to a lesser extent, physical infrastructure. Moreover, although local knowledge is recognized as important in principle for all aspects of disaster risk management, it is not frequently taken as a starting point for identifying contextually specific landslide risk factors in practice. To gain insights into local knowledge on factors contributing to landslide risk, we conducted a qualitative case study within a landslide-prone informal settlement in Manado, Indonesia. The study comprised qualitative household interviews, transect walks, and ethnographic observation. Our findings indicate that anthropogenic factors specific to informal settlements, particularly inadequate solid waste management, may serve as an overlooked risk factor for urban landslides. This is especially problematic in low-income or informal neighborhoods, where detrimental effects of poor solid waste management frequently intersect with heightened vulnerability to various hazards. We therefore advocate systematically incorporating anthropogenic factors into studies of contributing factors to urban landslides. Additionally, we encourage further research into the interactions between inadequate solid waste management and landslide risk. Finally, we call for leveraging local knowledge to enhance policy, planning, and research efforts, with the ultimate goal of fostering safer urban environments for all.

Landslide disaster risk analysis in Kebomas District, Gresik District

Background: Landslides occurred during the rainy season in Kebomas District, Gresik Regency, which has topography in the form of hills left over from limestone mining in 1960-1990. Field surveys conducted from December 2021 to March 2022 stated that landslides occurred during the rainy season in Kebomas District and caused damage to surrounding settlements. Slope, rainfall, rock type, soil type, and land use are factors that influence the occurrence of landslides. The purpose of this study was to map the level of landslide vulnerability in Kebomas District using a Geographic Information System. Method: The methodologies used in this study include the application of GIS for geospatial data analysis, assessment of landslide hazard potential through secondary data and field surveys, and vulnerability analysis of physical data. Additionally, a qualitative analysis of landslide disaster risk was conducted, combining hazard and vulnerability factors. These methods aim to identify and prioritize landslide-prone areas in Gresik Regency for better disaster risk management. Findings: The results showed that there were three levels of landslide vulnerability in Kebomas District, namely low, medium and high. The low vulnerability class is found in all villages in the Kebomas District, most of which are in the six villages or sub-districts in the south, west and north, namely; Dahanrejo, Kedanyang, Prambang, Kembangan, Randuagung, and Segoromadu. The moderate vulnerability class is found in all villages in the Kebomas District, most of which are in the six villages in the southeast, northwest and north, namely; Randuagung, Prambang, Dahanrejo, Kembangan, Sidomoro, and Ngargosari. Areas with a high vulnerability class are located in the central part of the sub-district which includes 15 villages or sub-districts, most of which are in four villages, namely; Klangonan, Kawisanyar, Randuagung, and Sidomoro. Conclusion: This study shows that mapping the level of landslide vulnerability in the Kebomas District can help plan and manage disaster risks more effectively, especially in areas with high vulnerability. Novelty/Originality of this article: The results of this study provide a basis for creating more targeted landslide mitigation and handling strategies, as well as the need for special attention in areas with moderate to high vulnerability.

Photogrammetry of airborne image set at high landslide disaster risk region using Meshroom and Blender

Abstract We report the application of the photogrammetry method to reconstruct three-dimensional topographic mesh at a high disaster risk region. This work is aimed to identify the landslide potencies of the area by means of slope analysis of the given area around the Bandung Basin rim. Our method proposes a low-cost aerial survey and an effective algorithm in order to map the local landslide tendencies. In order to perform image processing and 3D representation we utilized the photogrammetric pipeline in the Meshroom software. The post-processing of the 3D model was performed using Blender. In this research, we use a commercial camera drone as an image capturing device. As a test case we processed 183 images of an area of 51 m². The image-processing takes about 39 minutes on a 12 cores computer accompanied with a GPU card. From this set of images, we have yielded a three-dimensional slope structure comprising a total of 85,850 mesh nodes. As the result of this research, we propose the optimization of the image set and a list of post processing works of the nodes that can be used in order to characterize landslide disaster risk of the disaster-prone region in a fast and accurate manner.

Earthquake And Landslide Disaster Risk Assessment At The University Of Defense: A Preliminary Study

The geographical location of the University of Defense in Bogor Regency, Citereup Sub-district, Bogor, West Java, is a sloping terrain that can potentially cause landslides. The InaRISK map released by BNPB, shows that the Defense University is located in an area of Bogor Regency that is prone to disasters, especially landslides. Disaster risk assessment is an approach to show the potential negative impacts that may arise due to a potential disaster. The potential negative impacts are calculated based on the level of vulnerability and capacity of the area. This potential negative impact is seen from the potential number of people exposed, property loss, and environmental damage (PERKA BNPB, 2012). Researchers conducted an analysis of landslide and earthquake disaster vulnerability at the University of Defense. A study of building and population vulnerability to potential earthquakes and landslides was conducted at the University of Defense. The components of social vulnerability at the University of Defense in the event of an earthquake and landslide were obtained. Researchers obtained data on population capacity and building conditions at the University of Defense. Landslide and earthquake disaster risks have the potential to occur at the University of Defense. The University of Defense has the potential threat of landslides because the buildings of the University of Defense are located on hilly slopes. The vulnerability of the University of Defense buildings, the University of Defense buildings have not been designed to be entirely earthquake resistant. This can be seen in some buildings that have visible cracks. It is necessary to retrofit or strengthen the foundations of buildings in the Defense University area that have experienced a decrease in performance such as cracking, shifting, displacement. An important component where the University community and students need self-protection and rescue skills through training.

Utilization of Frequency Ratio and Logistic Regression Model for Landslide Susceptibility Mapping in Bogor Area

Java Island holds the highest record of landslide events in Indonesia. In 2021, the Bogor area, consisting of the city and regency of Bogor, recorded the highest number of landslides. These events further impact the fatalities, damage, and loss to society. Landslide mitigation should be considered to reduce the risk caused by landslide hazards. In this regard, a landslide susceptibility analysis is one of the fundamental steps in mitigation measures that can support policymakers in response to landslide disaster risk reduction. The location of landslide possibilities can be identified by mapping landslide susceptibility. Therefore, this study aims to produce a landslide susceptibility map (LSM) using a statistical frequency ratio method and logistic regression. The number of landslide inventories used in the model is about 822 events. To apply the model, the present study evaluates 13 influencing factors consisting of elevation, slope angle, slope aspect, slope curvature, Topographic Wetness Index (TWI), distance to river, lithological, distance to fault, soil type, annual rainfall, Normalized Difference Vegetation Index (NDVI), land use land cover (LULC), and road distance. The model performance is further evaluated using Area Under the ROC Curve (AUC). The frequency ratio (FR) and logistic regression (LR) models produce satisfactory results and have high predictions of future landslide occurrences with a score of 0.8317 and 0.8817, respectively.

Earthquake And Landslide Disaster Risk Assessment At The University Of Defense: A Preliminary Study

Earthquake And Landslide Disaster Risk Assessment At The University Of Defense: A Preliminary Study

Landslide hazard assessment and agricultural vulnerability using a geospatial approach

The village of Cukilan, Semarang Regency, Indonesia, is predominantly utilized for agricultural purposes, facilitated by its high rainfall suitable for various crops. Despite the benefits it provides to farmers, the elevated rainfall also poses natural disaster risks, particularly landslides, which can adversely affect surrounding communities. This study aims to map landslide potentials and evaluate their impacts on the agricultural sector in the Cukilan Village, Semarang Regency. The research methodology encompasses a literature review focusing on landslide potential estimation and field survey. The literature review aims to obtain landslide potential distribution maps compiled by DVMBG, considering rainfall, slope, geology, land use, and soil types. Field surveys validate conditions based on landslide potential estimations, involving random soil sampling and analyzing parameters like permeability, texture, bulk density, depth, and surface density. The research findings reveal the impact of landslide potential on the agricultural sector and the mapping of landslide potential areas in Cukilan Village, depicting various risk levels: very high (0.71% of total area, covering 5 ha), high (13.24%, 93 ha), moderate (56.55%, 397 ha), and low (29.48%, 207 ha). Based on these findings, it is evident that managing landslide disaster risks in agricultural development in the area is crucial, including the implementation of safer land use planning and appropriate mitigation strategies.

Dams overtopping scenarios from catastrophic landslides in mountains’ headwaters Case study in Kobe City, Japan

As a Mw 8.0 Nankai Trough Earthquake is predicted with an 80% probability of occurrence within the next 30 years, the efficiency of check dams in the mountains above Kobe City is a crucial question when considering co-seismic landslide disaster risk management. In the present contribution the author aimed to define which subsection of the Sumiyoshigawa watershed may be more prone to generate impacts in downstream Kobe City from catastrophic landslides in the headwaters. For this purpose, the present state of the check dams network was analysed from the 2018 LiDAR (Light Detection and Ranging) data, considering the progressive infilling of the structures. As a result, the capacity of the dams in 2018 dropped by about 1/10 of the original designed capacity, arguably because of the heavy rainfall events in 2014 and 2018. Despite this evolution, however, landslides > 20,000 m3 starting from only one tributary can fill all the dams and flow downstream. From this data, it is possible to prioritize dam curation programs, especially because population ageing and shrinking is reducing the manpower and the funds available to maintain the check dams.

Disaster Management and Landslide Risk Reduction Strategies in The Gebog Sub-District of Kudus Regency, Central Java, Indonesia

Historically, the Kudus Regency in Central Java Province, Indonesia, has been prone to landslides. This study was conducted in the Gebog Sub-District of Kudus Regency, Central Java, Indonesia. The objectives of this study are: (1) to analyze the level of landslide disaster risk in the Gebog Sub-District, (2) to assess the current community-based landslide risk management activities undertaken by the local population in the Gebog Sub-District and (3) to formulate community-based landslide risk reduction management strategies to mitigate landslide risk. Variables for the level of landslide disaster risk include landslide hazards/threats, vulnerability to landslide hazards and community capacity to deal with landslides. Variables related to the management of landslide risk studied encompass pre-disaster, during-disaster and post-disaster activities by the community. Meanwhile, the variables for community-based landslide risk reduction management strategies include threat reduction, vulnerability reduction and enhancing community capacity before, during and after a landslide disaster. The research design is based on field surveys with 99 individuals as research subjects and 33 field units as samples. Data was collected through questionnaires and interviews and analysis was conducted using scoring and ranking. The research findings indicate that the current community-based landslide risk management activities are still low. From the research and focus group discussions, new strategies for community-based landslide risk reduction management were identified to reduce landslide risk.

Landslide disaster risk analysis at Karanganyar district, Central Java province

The condition of Karanganyar Regency is formed by fragile and volcanic rocks weathered soil thickness and supported by high rainfall is a condition that has the potential to cause landslides. increasing population and intensive land use led to high risk of landslides. Landslides have occurred many times in the district Karanganyar. A combination of anthropogenic and natural factors often becomes cause of landslides that killed local residents and property loss. Large landslides have occurred in Karanganyar on December 26, 2007 which claimed 62 people died world. Landslide risk analysis research was carried out by entering several main components, namely hazard, susceptibility, and capacity. A qualitative approach was applied to carry out the analysis in the districts Karanganyar. The output of this study is a landslide risk map. Avalanche risk analysis was carried out at 3 selected locations experience landslides and the potential for such disasters in the Regency Karanganyar. The results of research on this topic are expected to be able to socialized so that local governments are able to develop plans sustainable development.

Research on Landslide Disaster Risk Assessment Method Based on GIS and Information Model

In this paper,a geological disaster early warning model is constructed based on GIS,and a susceptibility evaluation system is constructed by using GIS spatial information system to obtain seven influencing factors,including slope,topographic relief,slope shape,rock and soil mass type,geological structure,land use type and vegetation coverage rate.The information quantity value of each influencing factor is determined based on the information model method.At the same time,the hidden points of geological disasters in research area are superimposed and the prone zones are divided to provide reference for the prediction and prevention of geological disasters in this area.

Peran Vital Jurnalisme Warga dalam Pemulihan Pasca Bencana Tanah Longsor di Cianjur

This research discusses the crucial role of citizen journalism in the context of landslide disaster risk in Cianjur by applying a qualitative approach and analytical descriptive research methods. The results of the in-depth analysis highlight the importance of citizen journalism in empowering local communities with actual information, mobilizing support, and building solidarity in crisis situations. Although several hyperlocal social media accounts provide information, the involvement of citizen journalists in covering and informing post-disaster conditions is still minimal. In the context of mitigation and recovery, citizen journalism can play an important role in conveying local perspectives, monitoring the environment, and responding to specific community needs that may otherwise be overlooked. Evaluation of the impact, challenges and opportunities of citizen journalism in dealing with the landslide disaster in Cianjur is important to holistically understand its role and potential contribution in strengthening the sustainability and resilience of communities in the future.

A landslide susceptibility assessment method based on auto-encoder improved deep belief network

Abstract The landslide susceptibility assessment is an essential part of landslide disaster risk identification and prevention. However, the binarization of the hidden layer limits the parameterization ability of the conditional probability of visible layer, making the training process of restricted Boltzmann machine more difficult and further limiting the accuracy and efficiency of deep belief network (DBN) model in landslide susceptibility assessment. Therefore, this study proposed a landslide susceptibility assessment method based on Auto-Encoder (AE)-modified DBN. Zhouqu County, Gansu Province in the People’s Republic of China, was selected as the study area. Historical landslides in Zhouqu County were identified using small baseline subset interferometric synthetic aperture radar technology and optical image. Landslide factors were screened based on a geographical detector and stepwise regression method. The Logcosh loss function and determinant coefficient R 2 index were used to evaluate the training process of the AE model, and the balanced cross entropy loss function was used to evaluate the entire network training process. In addition, the area under the curve (AUC) of the synthetical index model (SIM), support vector machine (SVM), and multilayer perceptron (MLP) were compared and evaluated. The results indicated that the proposed model could significantly improve the accuracy of landslide susceptibility assessment. The AUC value of the proposed model was 0.31, 0.12, and 0.11 higher than that of SIM, SVM, and MLP, respectively. Therefore, the improved DBN model based on AE proposed is reliable for early landslide identification and prediction.

Landslide disaster risk analysis in Pacet District, Mojokerto Regency, East Java

Pacet District, Mojokerto Regency, East Java is one of the areas prone to landslides. Landslides in the area that occur can threaten lives and cause environmental damage and financial losses. This study aims to analyze the risk of landslides in Pacet District, Mojokerto Regency, East Java, and apply Early Warning System (EWS) technology based on the Internet of Things (IoT). This type of research is both qualitative and quantitative, the results of the study are based on the level of threat of landslides. Designing a landslide early warning tool is not easy to do because it has to be adapted to local situations and conditions. This program will be directed at technology transfer by implementing an Internet of Things (IoT)-based Early Warning System (EWS).

3DCNN landslide susceptibility considering spatial-factor features

Effective landslide disaster risk management contributes to sustainable development. A useful method for emergency management and landslide avoidance is Landslide Susceptibility Mapping (LSM). The statistical landslide susceptibility prediction model based on slope unit ignores the re-lationship between landslide triggering factors and spatial characteristics. It disregards the influence of adjacent image elements around the slope-unit element. Therefore, this paper proposes a hardwired kernels-3DCNN approach to LSMs considering spatial-factor features. This method effectively solved the problem of low dimensionality of 3D convolution in the hazard factor layer by combining Prewitt operators to enhance the generation of multi-level 3D cube input data sets. The susceptibility value of the target area was then calculated using a 3D convolution to extract spatial and multi-factor features between them. A geospatial dataset of 402 landslides in Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province, China, was created for this study. Nine landslide trigger factors, including topography and geomorphology, stratigraphic lithology, rainfall, and human influences, were employed in the LSM. The research area’s pixel points’ landslide probabilities were then estimated by the training model, yielding the sensitivity maps. According to the results of this study, the 3DCNN model performs better when spatial information are included and trigger variables are taken into account, as shown by the high values of the area under the receiver operating characteristic curve (AUC) and other quantitative metrics. The proposed model outperforms CNN and SVM in AUC by 4.3% and 5.9%, respectively. Thus, the 3DCNN model, with the addition of spatial attributes, effectively improves the prediction accuracy of LSM. At the same time, this paper found that the model performance of the proposed method is related to the actual space size of the landslide body by comparing the impact of input data of different scales on the proposed method.

The Use of Geographical Information System (GIS) and Remote Sensing (RS) Technologies in Generation of Information Used to Mitigate Risks from Landslide Disasters: An Application Review

Landslides, whether induced by nature, or human activities, are one of the most prominent disasters which are of great concern in the world. They cause a lot of havoc to the environment hence a necessity to avoid them whenever possible. This literature review is basically aimed at finding out how Geographical Information System (GIS) and Remote Sensing (RS) technologies are used in avoiding landslides and risks associated with them at different levels; Challenges encountered while using GIS and RS techniques for landslide disaster risk reduction are identified; and therefore, dealing with them requires the involvement of developed countries who have the capacity to provide the necessary equipment to the developing countries that are faced with disasters. This study discusses the use of GIS and RS in mitigation of risks from landslides, and mainly points out how these techniques are applied to avoid disaster risks. Secondary data was reviewed from journal articles, institutional reports, and online publications from similar studies. GIS and RS tools are important in predicting, monitoring and managing landslide disasters. It was concluded that GIS and RS tools provide cheaper, reliable, and faster techniques of accessing spatial data in a given area, therefore regarded as essential technologies that may be necessary for predicting landslide occurrences, these technologies need to be considered in communities which are prone to landslides.

Roda de conversa sobre riscos de desastres associados a deslizamentos na Rocinha (Rio de Janeiro, Brasil): experiências de luta, resistência, saberes e arte

Os desastres associados a deslizamentos de terra revelam uma organização socioespacial desigual, caracterizada pela ocupação rápida e informal de áreas suscetíveis a estes eventos adversos. Estes afetam principalmente populações mais pobres com elevado grau de vulnerabilidade, cujas demandas sociais são frequentemente negligenciadas pelo poder público. O presente trabalho, portanto, visa apresentar e discutir a experimentação do método roda de conversa para troca de saberes entre acadêmicos e representantes da população exposta na comunidade da Rocinha, uma das favelas do Rio de Janeiro. Trata-se de uma adaptação do método photovoice para a promoção de debates entre acadêmicos das áreas de antropologia, engenharia e sociologia com moradores da favela, tendo sido associado a uma intervenção artística. A roda de conversa contribuiu para a emancipação dos diferentes sujeitos ao proporcionar a interação reflexiva a partir de suas experiências vividas de luta, resistência e produção de saberes. Além disso, as imagens usadas suscitaram discussões sobre temas, tais como reassentamento, políticas públicas locais e solidariedade entre moradores. Por último, o olhar artístico traduziu o debate na criação de um grafite que ficou de legado na comunidade.

Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

The interaction of seismic events with geo‐environmental conditions and anthropogenic activities may exacerbate the risk of landslide hazard in a mountainous region. As an example of this, the 2005 Kashmir earthquake triggered many shallow to deep slope failures, which were further intensified in the following years by human activities notably along road networks, posing a long‐term hazard. Hence, this study was planned to evaluate the effectiveness of landslide susceptibility prediction along an earthquake‐affected road section of Neelum Highway using six different data‐driven models. We applied analytical hierarchy process as a heuristic approach, weight of evidence and index of entropy as statistical models and multi‐layer perceptron, support vector machine and binary logistic regression (BLR) as machine learning models. Initially, 224 landslide locations were marked through field surveys to prepare a landslide inventory, which was further randomly divided into training (70%) and testing (30%) datasets. Then, 13 landslide causative factors (LCFs) were extracted from geospatial database and analysed by measuring collinearity among factors and assessing their contribution to landslide occurrence using different feature selection methods for inclusion in susceptibility modelling. Thereafter, six employed models were trained to produce landslide susceptibility maps of the investigated road section. Finally, the area under receiver operating characteristics (AU‐ROC) curve and various statistical measures were applied to validate and compare the performance of modelled landslide susceptibility. The results revealed that no collinearity issue exists among all 13 LCFs, and all six models exhibited satisfying performance in predicting landslide susceptibility of study area. However, BLR model has produced the most promising and optimum results as compared with other models with AU‐ROC (0.881), Matthew's correlation coefficient (0.609), Kappa coefficient (0.604), accuracy (0.797) and F‐score (0.787). The outcomes of this study can be used as pertinent guide for preventing and managing the landslide disaster risk along Neelum Highway and beyond.