Causes Of Landslides Research Articles

Landslide Identification on Tembalang-Jangli New Road, Semarang, Central Java Using Dipole-Dipole Configuration Resistivity Geoelectric Method

New roads are built to alleviate congestion that occurs in an area. Tembalang is one of the most densely populated areas in Semarang city. The research location is Tembalang-Jangli New Road. The construction of Jangli-Undip New Road aims to reduce congested vehicle traffic in Gombel and Meteseh, which causes frequent congestion. Therefore, subsurface identification using the resistivity geoelectric method of dipole-dipole configuration was carried out to know the cause of landslides at the research location. Data acquisition was carried out with 10 passes. The results obtained 5 (five) layers of subsurface constituent rocks on Undip-Jangli New Road based on their resistivity values, namely topsoil (0.105 - 1.0 Ωm), clay (1.11 - 6.29 Ωm), sandstone (8.52 - 20.0 Ωm), tuff (20.0 - 75.0 Ωm), and volcanic breccia (75.0 - 133 Ωm). The interpretation results show the sliding plane at a 6.76 - 24.8 m depth. The layer that acts as a slide plane is a clay layer. The existence of sliding planes detected in the trajectories A-A’, B-B’, C-C‘, D-D’, E-E‘, F-F’, G-G‘, H-H’, I-I‘, and J-J’ has great potential to cause soil movement or landslides with the type of sliding is rotation. Keywords: landslide, resistivity geoelectricity, dipole-dipole.

Analysis of the Causes of Landslide Wave Movement Process (Using the Example of Nookat District of Osh Region)

Analyzing the causes and consequences of landslides is important for developing effective measures to prevent and manage them, aiming to ensure the safety of the population and the preservation of the environment. Two main causes of landslides are identified: natural and anthropogenic. In general, the study of landslide processes has broad practical interest, including aspects of safety, ecology, construction, and adaptation to changing climatic conditions. The article discusses the main causes of landslide processes Nookat region and analyzes the reasons for landslides.

Study of Earthquake Landslide Hazard by Defining Potential Landslide Thickness Using Excess Topography: A Case Study of the 2014 Ludian Earthquake Area, China

Influenced by the combined effects of crustal uplift and river downcutting, rivers with significant potential energy are often found in high mountain and canyon areas. Due to the active tectonic movements that these areas have experienced or are currently experiencing, geological hazards frequently occur on the mountains flanking the rivers. Therefore, evaluating the susceptibility and risk of earthquake landslides in river segments of these high mountain and canyon areas is of great importance for disaster prevention and mitigation, as well as for the safe construction and operation of hydropower stations. Currently, a major challenge in the study of landslide susceptibility and hazard is determining the thickness of potential landslide bodies. The presence of excess topography reflects the instability of the disrupted slopes, which is also a fundamental cause of landslides. This study takes the example of the Ludian earthquake in 2014, focusing on the IX and VIII intensity zones, to extract the excess topography in the study area and analyze its correlation with seismic landslides. The correlation between the critical acceleration value and the excess topography was validated using the Spearman’s rank correlation coefficient, resulting in a correlation coefficient of −0.771. This indicates a strong negative correlation between the excess topography and critical acceleration, with significant relevance. The landslide susceptibility distribution obtained by setting the potential landslide thickness based on the excess topography and proportion coefficient showed an ROC curve analysis AUC value of 0.829. This is higher than the AUC value of 0.755 for the landslide susceptibility result using a uniform potential landslide thickness of 3 m, indicating the higher model evaluation accuracy of this approach. Earthquake landslide hazard predictions for rapid post-earthquake assessments and earthquake landslide hazard zoning for pre-earthquake planning were made using actual seismic ground motion and a 2% exceedance probability in 50 years, respectively. Comparing these with the 10,559 coseismic landslides triggered by the Ludian earthquake and evaluating the seismic landslide development rate, the results were found to be consistent with reality. The improved model better reflects the control of excess topography and rock mechanics properties on the development of earthquake landslide hazards on high steep slopes. Identifying high-risk seismic landslide areas through this method and taking corresponding preventive and protective measures can help plan and construct safer hydropower and other infrastructure, thereby enhancing their disaster resistance.

Balancing Submarine Landslides and the Marine Economy for Sustainable Development: A Review and Future Prospects

To proactively respond to the national fourteenth Five-Year Plan policy, we will adhere to a comprehensive land and sea planning approach, working together to promote marine ecological protection, optimize geological space, and integrate the marine economy. This paper provides a comprehensive review of the sustainable development of marine geological hazards (MGHs), with a particular focus on submarine landslides, the marine environment, as well as the marine economy. First, the novelty of this study lies in its review and summary of the temporal and spatial distribution, systematic classification, inducible factors, and realistic characteristics of submarine landslides to enrich the theoretical concept. Moreover, the costs, risks, and impacts on the marine environment and economy of submarine engineering activities such as oil and gas fields, as well as metal ores, were systematically discussed. Combined with the current marine policy, an analysis was conducted on the environmental pollution and economic losses caused by submarine landslides. Herein, the key finding is that China and Mexico are viable candidates for the future large-scale offshore exploitation of oil, gas, nickel, cobalt, cuprum, manganese, and other mineral resources. Compared to land-based mining, deep-sea mining offers superior economic and environmental advantages. Finally, it is suggested that physical model tests and numerical simulation techniques are effective means for investigating the triggering mechanism of submarine landslides, their evolutionary movement process, and the impact on the submarine infrastructure. In the future, the establishment of a multi-level and multi-dimensional monitoring chain for submarine landslide disasters, as well as joint risk assessment, prediction, and early warning systems, can effectively mitigate the occurrence of submarine landslide disasters and promote the sustainable development of the marine environment and economy.

Identification of sliding surface and classification of landslide warning based on the integration of surface and deep displacement under normal distribution theory

Advanced identification of the potential sliding surface of a slope and accurate early warning are crucial prerequisites for effective management of landslides and timely and prevention of catastrophic accidents. This study analyzes the statistical characteristics of landslide displacement evolution. Based on the normal distribution theory, random variables of displacement velocity and acceleration with random errors are introduced into the analysis of surface displacement information, and random variables of relative displacement with random errors are introduced into the analysis of deep displacement information. When the random variables do not follow the normal distribution, the warning time can be obtained. Therefore, an advanced landslide classification warning method is established. The analysis results showed that analysis results from the April 30 landslide project at an open pit mine indicate that the earliest warning time for landslide initiation is 2020/2/19, while the earliest warnings for acceleration occur on 2020/4/15 and the fast acceleration on 2020/4/25. These three-level warning times align with reality, and the inferred slip surface position corresponds to the actual weak layer range. The primary power source driving landslide originates from behind the sliding body which subsequently pushes rock mass along weak layers near the south wing, north wing, and front in succession. Research findings can enhance landslide warning accuracy, facilitate advance identification of sliding surface, provide scientific basis for open-pit slope engineering design, as well as mitigate casualties and property losses.

Geophysical and numerical approaches to solving the mechanisms of landslides triggered by earthquakes: A case study of Kahramanmaraş (6 February, 2023)

Many landslides occurred in the 11 Turkish provinces affected by the earthquakes centered in Kahramanmaraş in February 2023, and many people lost their lives due to those landslides. To reduce the risks and hazards brought on by landslides induced by earthquakes, it is essential to identify the mechanisms causing landslides to arise in such situations. In this study, the geological strength index and both seismic refraction tomography and electrical resistivity tomography (ERT) geophysical methods were used to determine the failure mechanism of layered sedimentary units defined as flysch (claystone-siltstone-sandstone-marl). The shear strength reduction finite element method (SSR-FEM) was used with the RS2 computer program for model solutions. One of the geophysical methods, ERT, facilitated an interpretation of the cyclic failure mechanism that occurs in flysch, and it was found to be congruent with numerical models. Landslide slip circle depth, water-saturated layers, and resistivity values were determined with the ERT method. Seismic methods were employed to determine the velocity values (Vp, Vs) of the landslip mass. These velocity data, along with the elasticity modulus and Poisson ratios derived from them, were utilized in our numerical model. In the maximum shear strength and horizontal displacement analyses performed on the section line determined before the landslide, the natural state and the situation under the influence of groundwater were examined and the strength reduction factor (SRF) was determined as 1.35 and 1.24, respectively. In an evaluation conducted under the influence of the maximum horizontal ground acceleration (0.22 g) determined by taking into account the seismicity of the study area, it was concluded that instability occurred in the flysches and the SRF value was 0.72. An important finding of this study is that the results of geophysical methods used to determine the lithological properties of sedimentary rock masses and understand the causes of landslides align closely with those of numerical analysis methods, providing high accuracy. These methods also support new approaches, particularly in identifying landslip boundaries and detecting areas with high water saturation. It has been discovered that earthquakes actively contribute to the instability of these kinds of soils.

Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product

Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m3/m3 saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization’s impact on landslide risks in developing nations.

An Accurate Recognition Method for Landslides Based on a Semi-Supervised Generative Adversarial Network: A Case Study in Lanzhou City

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km2, with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou.

Combining InSAR Technology to Uncover the Deformation Factors and Mechanisms of Landslides in the Baihetan Hydropower Station Reservoir Area

With the operation of the world’s second-largest hydropower facility, Baihetan Hydropower Station, the risk of landslide deformation has increased. To address these potential threats, we employed Interferometric Synthetic Aperture Radar (InSAR) technology for a large-scale landslide investigation and comprehensively revealed the deformation mechanisms of landslides near the dam site. Our research indicates that the alternating geological features of soft and hard rock layers are the primary causes of landslides, especially the fracturing phenomena of vast amounts of mudstone upon contact with moisture. This leads to the reservoir’s left bank’s dip-slope being susceptible to slip and tensional failure, while the reservoir’s right bank’s reverse slope is more prone to plastic flow and tensional damage. Rapid water level changes and altered rainfall patterns are key factors that trigger landslide instability. Furthermore, we also explored the relationship between fault zones, seismic activity, and landslides, particularly noting the fully coupled state of the southern end of the Daliangshan fault zone, which might further exacerbate landslide deformation.

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.

Role of rainfall temporal distribution on effective infiltration in the loess slope and prediction model

Rainfall is one of the main causes of landslides, particularly in the Loess Plateau. When rainfall occurs, rainfall thresholds can be used to predict whether it will cause landslides in monitoring and early-warning system. However, most rainfall thresholds are established based on constant rainfall processes. Therefore, this study takes the temporal distribution of rainfall into account in rainfall threshold studies and proposes four well-delineated temporal distribution types and an innovative three-step model. Rainfall thresholds are derived from 45 months of rainfall data and slope moisture data obtained through in-situ monitoring of the loess slope in Yan'an city, northwest China. Our analysis focuses on effective rainfall infiltration rather than shallow landslides as the monitoring index for thresholds. The results revealed a total of 189 rainfall events over the course of 45 months. Among these, 167 were classified as ineffective rainfall events with an infiltration depth of <10 cm, while 22 were categorized as effective rainfall events with an infiltration depth exceeding 10 cm, accounting for only 11.6% of the total. In this study, the temporal distribution of rainfall was classified into four types: transitory, constant, unimodal, and multimodal. Analysis of the monitoring data showed that transitory rainfall events were all effective, constant rainfall events were mostly effective (> 76%), unimodal rainfall events were 86.7% effective, and all multimodal rainfall events were effective, with the deepest infiltration depth. A three-step method was employed to establish thresholds for different types of rainfall. Firstly, cumulative rainfall exceeding 34 mm at a single time point was considered the most effective rainfall. Secondly, multimodal or constant rainfall with a constant value of Ac > 3.5 mm/3 h and a peak value of Ap > 9.5 mm/3 h was deemed the most effective rainfall. Lastly, a temperature difference Tr during rainfall below 7.7 °C was indicative of mostly ineffective rainfall. The model demonstrated an accuracy of 96%, as validated by existing monitoring data in the nearby loess area. This model has been demonstrated to be superior to previous model and is currently suitable for application in loess regions of China.

A century of urban landslides: the legacy and consequences of altering riverbank landscapes

Urban landslides are a deadly and costly hazard. Human actions, such as altering the grade, composition and vegetation-cover of hillslopes, can increase the threat of mass movements. Here, we use an interdisciplinary approach to examine the spatial distribution, timing and cause of landslides affecting a state highway and adjacent buildings along the top of a steep, urban riverbank in the mid-latitude, humid-temperate state of Vermont. Using over 100 years of mapping, photographs and written records, we demonstrate that most mass movements in our field area occurred on slopes oversteepened by the addition of uncompacted artificial fill, added without engineering considerations. Emplaced atop glacial and post-glacial sediment with low hydraulic conductivity, the fill, having little to no cohesion, expanded buildable areas, but the new infrastructure sat on unstable ground. Over the following decades, repeated failures ( n = 20), mostly shallow translational landslides in fill material along with several deeper-seated rotational slides, sent buildings, trees and segments of road into the river below. Solutions include incentivizing the removal of structures built on fill and limiting further filling activities through changes in zoning regulations and more effective enforcement of existing municipal codes. The approach we use provides a framework for similar geographical settings and can inform urban planning and risk assessment. Supplementary material : Supplementary photographs and imagery, and a list of references for all historical photographs and maps used in this study, are available at https://doi.org/10.6084/m9.figshare.c.7103608 Thematic collection: This article is part of the Engineering Geology and Hydrogeology of the Anthropocene collection available at: https://www.lyellcollection.org/topic/collections/engineering-geology-and-hydrogeology-of-the-anthropocene

Landslide susceptibility evaluation based on landslide classification and ANN-NFR modelling in the Three Gorges Reservoir area, China

The Chongqing section of the Three Gorges Reservoir area (TGRA) is a high-risk geological catastrophe warning zone in the Yangtze River Basin. This study aimed to categorize landslides and improve the accuracy of landslide susceptibility evaluations by combining the artificial neural networks (ANN) and the normalized frequency ratio (NFR) models. Considering that the indicator factors have different effects on various landslides, the landslides were separated into four types, that is, giant, large, medium, and small. Thirteen indicator factors were selected through correlation analysis, including elevation, lithology, precipitation, land use, population density, and so on. Combined with 7777 historical landslide events, an ANN-NFR coupling model was proposed. The susceptibility grade prediction of landslides for the entire region was conducted using a GIS platform. Compared to the NFR model, the ANN-NFR model could improve the accuracy of landslide susceptibility evaluation by approximately 4%. This indicates that the ANN-NFR model is an effective method to calculate the weights of indicator factors. The FR value of large landslides is 9.201 in the very susceptibility region, and the model evaluation effect is superior to that of the other three types of landslides. The success rate of the ANN-NFR model after landslide classification was 78.9%, and the prediction rate was 78.6%, both of which were greater than the unclassified ANN-NFR model. Therefore, the ANN-NFR (landslide classified) model could improve the prediction accuracy and can provide a scientific basis for disaster prevention, mitigation, and management in the TGRA.

Geological, geophysical, and geotechnical approaches on the slip surface investigation on Cisumdawu Highway, West Java, Indonesia

This study used geological, geophysical, and geotechnical methods to investigate the causes of landslides in a newly established Cileunyi Sumedang Dawuan (Cisumdawu) Highway, Sumedang Regency, West Java Province. A combination of geological field observation, subsurface geoelectric resistivity-based survey, and borehole drilling were applied to obtain the subsurface data of a research area. A total of 10 boreholes were drilled to collect geoengineering data, including the soil material and N-SPT value for validation purposes. The soil hardness and resistivity were measured and compared to establish the relationship between resistivity and engineering properties. The result revealed that percolating water zone in the permeable loose soil was located above the impermeable layer, estimated as a slip surface. The analysed subsurface measurement and borehole data showed that the depth of the slip surface is between 26-27m. The study is important to determine the mitigation steps for the highway slope stability construction.

Ground Water of Himalayan Region, Due to Landslides Impact on Himalayan Folk Life

Looking at the life of the Himalayan people, the ground water level there is continuously decreasing. The problem of landslides is affecting the life of the people there. The speed with which the perennial streams are drying up, the Himalayan people life is in danger due to disasters, due to which the solution is very important. Geologists are conducting surveys from time to time to clarify the cause of landslides and drying up of streams. There are indications of a serious accident. Due to the rivers originating from the Himalayas, the pressure of water and sediment increases, due to which the pressure on the slopes also increases, whereas the rivers of the Western Ghats have less sediment as compared to the Himalayan rivers.

Landslides: How, Why and the Way Forward

Slopes sometimes slide and cause damage and disruptions for the public. Landslides can be triggered by a combination of natural factors such as intense and prolong rainfall, soil properties and steep gradient slope. Human activities such as deforestation, improper construction practices and inadequate drainage systems can also contribute to landslide occurrences. Landslide mitigation is crucial to reduce the loss of lives, protect infrastructures, and ensure public safety. Given that landslides occur more frequently during intense and prolong rainfall, it's important to have effective strategies in place to minimize their impacts. This paper aims to provide insights into the causes of landslides, their impacts, and effective strategies for mitigation. Examples of successful landslide mitigation and management initiatives are included. The comprehensive approach, which involves education, research, planning, and collaboration, has the potential to significantly mitigate landslides. The way forward is to establish a centralized slope agency to lead and assist local authorities is an important strategic move. This agency as an One Stop Centre (OSC) could provide expertise, guidance, and control for slope-related projects, ensuring that proper standards are met. The Centre also provides risk assessment of existing slopes, mitigation measures and carries out research and development on improving slope engineering. Collaboration between government agencies, academic institutions, industry professionals, and local communities is key to the success of landslide mitigation efforts.

Refined InSAR method for mapping and classification of active landslides in a high mountain region: Deqin County, southern Tibet Plateau, China

The mapping and classification of active landslides in high mountainous regions provide crucial information about the location and types of geohazards. Additionally, this process plays a vital role in ensuring the safety of the geological environments in mountainous towns. In this study, we presented a refined InSAR approach for mapping and classifying active landslide hazards in Deqin County, Tibetan Plateau, China. The study area is characterized by a high altitude and extremely rugged terrain. Consequently, conventional InSAR methods are limited in precisely estimating landslide deformation owing to severe atmospheric delays. To this end, we first propose a block-based linear model to correct tropospheric artifacts. This model considers the spatial variability of the atmosphere and provides an opportunity to accurately estimate heterogeneous atmospheric delays over high mountainous areas without any external data. Compared with the traditional global-window linear model and the GACOS approach, the new method demonstrated outstanding performance in reducing atmospheric artifacts. Second, based on the knowledge mapping of landslide types, we proposed a semi-automatic procedure to map and classify landslides using InSAR-derived displacements and auxiliary data (i.e., C-index and high-resolution optical images). Our results obtained from ascending and descending Sentinel-1 images revealed, for the first time, that there were 317 active landslides in Deqin County between May 2017 and June 2021. Among these, 10.7% were associated with slide activity, 7.9% with fall deformation, and the majority (81.4%) with flow movement. These results were cross-verified and evaluated using an a priori inventory map obtained from the visual interpretation of optical images and geological field surveys. This study demonstrates that InSAR can accurately map and classify active landslides over difficult mountainous terrains, provided the associated phase errors are effectively restrained.

Landslide susceptibility assessment along the Red Sea Coast in Egypt, based on multi-criteria spatial analysis and GIS techniques

The Egyptian Red Sea coast, a mountainous coastal region, is periodically exposed to landslide that cause severe man and economic losses. That is due to its geological, hydrogeomorphological, and seismological nature. This research aims to map landslide susceptibility in the study area using a multi-criteria overlay analysis approach. Slopes, elevations, aspects, curvature, lineaments, faults, earthquakes, rainfall, stream network, and rock units are the factors used to derive the landslide's susceptibility map. The data were obtained from research organizations and open-source geospatial platforms. The landslide susceptibility map of the area under investigation ranges from low to high as follows: 1) low (23 %), 2) moderate (43 %), and 3) high (34 %) from total area. The used model is validated using direct field check and statistical analysis by Receiver operating characteristic (ROC) curve test 40 landslides events. Based on ROC, the multi-criteria overlay analysis approach can predict landslides by 82 % at a 95 % confidence level. The leading causes of landslides are geological, topographical, hydrogeomorphological, and seismological. For long-term development, the study's findings would assist decision-makers in lowering the risks connected with landslides in the areas. To reduce the chance of landslides, the study recommended it is important to take into account the rugged topography of zones where landslides are likely to occur and implement rockfall protection systems like barriers, embankments, or mesh.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models, Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB), landslide models were constructed. Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic (ROC) curves. The results showed that LR, RF, SVM, and XGB models have good predictive performance for landslide susceptibility, with the area under curve (AUC) values of 0.752, 0.965, 0.996, and 0.998, respectively. XGB model had the highest predictive ability, followed by RF model, SVM model, and LR model. The frequency ratio (FR) accuracy of LR, RF, SVM, and XGB models was 0.775, 0.842, 0.759, and 0.822, respectively. RF and XGB models were superior to LR and SVM models, indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.©2024 China Geology Editorial Office.

Pembuatan Peta Resiko Bencana di Desa Kubang Tangah Kota Sawahlunto dengan Metode Arc Toolbox

Landslide movement is the activity of a balance disturbance process that causes the movement of masses of soil and/or rock from higher to lower areas. The cause of landslides is that the driving force on the slope is greater than the resisting force. Several factors cause landslides such as rainfall, steep slopes, soil density, rock type, land use and vibration. Sawahlunto City is one of 19 cities/regencies in West Sumatra Province that has a high level of vulnerability to landslides. Areas prone to landslides can be identified by utilizing remote sensing data and geographic information systems. This service is carried out by making maps of areas prone to landslides using parameters that cause landslides, including rainfall, soil type, height, slope and land use. These parameters will be processed and analyzed with the help of the ArcToolbox method, where ArcToolbox can be used to detect landslide areas in Kubang Tangah Village. The analysis process is carried out by using the spatial analysis tools feature found in ArcGIS software. Community service produces a slope map that can illustrate the vulnerability of landslides. Obtain results in the form of a landslide vulnerability level map which has 4 vulnerability classes, including non-prone vulnerability class, low vulnerability class, medium vulnerability class, and high vulnerability class.