Landslide Events Research Articles

Spatial Analysis of Causative Factors for Landslide Susceptibility on Java Island

Landslide is the most frequent natural disaster on Java Island with around 7300 occurrences in 1998-2023 (BNPB), causing 1807 fatalities and 45770 infrastructure damages. Landslide susceptibility modeling can be implemented as a basis for landslide risk modeling in Java Island for mitigation purposes. This research aims to model landslide susceptibility in Java Island and validate it based on historical landslide occurrence data. The methodology used for the landslide susceptibility model is based on the relationship between the distribution of landslide events and the causative factors in each of their classification class. Meanwhile, the methodology used for validation is Receiver Operating Characteristic (ROC) curve analysis, conducted by comparing the landslide susceptibility model with landslide occurrence data to obtain the area under the curve (AUC) value that shows the performance of the model. In this study, historical landslide occurrence data from two different sources, NASA and BNPB, were used to generate two separate models to see the difference of performance between both models. Each landslide occurrence data from both sources is divided into two parts; 70% of it is used to develop landslide susceptibility map and the rest is used for validation process. As many as ten causative factors were used to generate the model; elevation, slope, aspect, lithology, land cover, rainfall, river density, PGA (Peak Ground Acceleration), and distance to fault and river. The results show that based on landslide susceptibility map from BNPB data, Java Island is dominated with low susceptibility, that is about 58447 km2. Meanwhile, based on landslide susceptibility map from NASA data, Java Island is dominated by medium susceptibility, that is around 76731 km2. The performance for the models based on AUC values are 0.98-0.99 successful for NASA’s dataset and 0.91-0.92 successful for BNPB’s dataset in assessing landslide susceptibility in Java Island.

Distribution of the Surface Ruptures in the Kalaotoa Due to the 2021 M w 7.3 Flores Sea Earthquake, Indonesia

A magnitude 7.3 earthquake occurred at precisely 11:20 on December 14, 2021, within the Flores Sea region of Indonesia, precipitating a consequential landslide event. This seismic incident induced the creation of an extensive rupture zone spanning approximately 8 kilometers along the Kalaotoa fault (KF) system, which exhibits an NNE-NW-SSE trend. The objective of this study was to concentrate specifically on the Kalaotoa Island vicinity, situated approximately 40 kilometers from the earthquake’s epicenter, encompassing meticulous orientation and comprehensive field surveys conducted during the reconnaissance phase. The results of the ground assessment have been combined with spatial analysis data to ascertain the configuration and dispersion of ruptures subsequent to the earthquake, thus delineating the affected region as a prospective area susceptible to seismic events and subsequent calamities. In the vicinity proximate to the northern epicenter, fractures resulting from the seismic activity were consolidated, exhibiting considerable devastation in the form of ground fissures, rock avalanches, landslides, and structural collapses, and were predominantly oriented in a roughly NE-SW direction. Notably significant are the southern beaches of Lato’do and the eastern extremity of the rupture zone in the Lembang Mate’ne area, where coastal transgression and extensive inundation during high tides have been observed due to subsidence of 1.2 meters since the 2021 Flores Sea earthquake. Horizontal displacement around Kalaotoa Port, north of the Karumpa-Kawawo area. Displacement distribution, slip sense, and crustal deformation after earthquake propagate unilaterally to the north, resulting in a movement of the northeast side of the fault concerning SE.

Spatial distribution patterns of dated landslide events in the Northern Apennines in response to Holocene regional climatic changes

In this work we examine the spatial and temporal distribution of radiocarbon dates from landslides on the NE-facing side of the Northern Apennines during the Holocene, focusing on the region’s associated vegetation and climatic changes. We also propose a method for identifying areas and landslides that may contain very old organic materials for future research in the region. This analysis was based on a principal component analysis (PCA) on 5255 grid cells measuring 1 km2 each, overlaid on the study area. These grid cells incorporated variables derived from the terrain characteristics, including underlying lithologies and topographic features.Our findings reveal that, during the upper Holocene, the last dated events of recurrent and single-event landslides occurred first closer to the coldest and wettest territories once occupied by the glacier termina during the Last Glacial Maximum (LGMgt). Subsequently the last dated events occurred at distances greater than 30 km from the LGMgt, towards the mountain chain’s margin. Conversely, at the beginning of the Holocene, an opposite pattern emerged, with landslide events occurring towards the chain interior. These spatial patterns were consistently observed when considering also the entire dataset of 87 dates for landslide events. During the middle Holocene, we observed that the recurrent landslides were primarily concentrated within a narrow belt 8.5 km from the LGMgt. Furthermore, higher rates of landslide activity were associated with the 4.2 ka BP event and persisted until approximately 2.0 ka BP, indicating a correlation with wet and cool periods. In conclusion, these spatial patterns in landslide events can be linked to changes in precipitation patterns and dominant atmospheric circulation in the mountain belt occurred since the beginning of the Holocene.

An integrated numerical model for landslide-generated waves: a case study of the Gongjiafang landslide, Three Gorges Reservoir, China

ABSTRACT Landslide is a frequent geological hazard in the Three Gorges reservoir area of China. Impulse waves could be created and cause substantial damage if the landslide collides with a water body. A nonlinear shallow water equation in combination with a progressive landslide model is proposed to simulate this catastrophic disaster. The proposed approach is distinguished by rapid calculation, less computational burden, and high practical value. By simulating the 2008 Three Gorges Reservoir Gongjiafang landslide and the subsequent impulse waves, we illustrated and validated this methodology. The initiation and motion processes of the landslide, as well as the generation, propagation, and run-up processes of impulse waves, were thoroughly performed. The simulation results were in good agreement with the field survey data, demonstrating the viability of the proposed approach for simulating the impulse waves triggered by landslide events. This numerical approach is beneficial for engineers to perform a preliminary estimation of landslide-generated waves and further facilitate the prevention and mitigation of geological hazards.

Estimating the period of probable landslide event using advanced D-InSAR technique for time-series deformation study of Kotrupi region

Himalayan region is prone to landslides and every year many landslides occur. Some of these events are reported while, many of these landslides are not reported due to their remote location of occurrence, or nature of impact, and its date of occurrence is also unknown. This study tries to find a method to estimate the probable dates of landslide occurrences using time-series deformation analysis applying advanced DInSAR approach by adopting a short temporal baseline graph. We used 118 Sentinel 1-A SLC images to identify the nature, pattern, and trends of landslide deformation. Based on the time series cumulative deformation values, we found a ‘V’-shaped kink that matches with the landslide events. Such kinks were observed around 12 June 2015, 25 May 2016, 05 August 2016, 23 June 2017, 15 September 2019, 27 May 2018, 08 June 2018, and 15 July 2020. The majority of these kinks are visible during monsoon season which is expected. High-resolution Planet satellite data around these time ranges were used to verify the existence of new landslides. Moreover, the performance is evaluated using AUC-ROC, precision, recall; and F-Score. This approach has good prediction capabilities with the precision of 87%, AUC-ROC of 77% and F-score of 74%.

One-dimensional numerical model of landslide over high-steep slopes: Theory and verification to the analytical solution

At the steep slope areas, landslide events may suddenly occur due to prolonged heavy rain and seriously affect humans’ lives and their properties. Numerical models simulating landslides help us to predict the real landslide phenomenon effectively because they are not only time-saving compared to physical models but also acceptable accuracy. In this paper, the one-dimensional nonlinear shallow water equations using the global coordinate system are established as the proposed model governing equations. The hybrid finite difference-finite volume method, the implicit method and the automatical time step controlling technique are employed to stably simulate the landslide events over the high-steep slopes. To validate the proposed numerical model to the analytical solution, authors continuously derive the non-dimensional governing equations in one dimension as well as using the global coordinate system. The numerical results are in good agreement with the analytical solution. The proposed numerical model can be used to simulate the landslides and the landslide-induced tsunami events, especially for simulating the high-steep slope conditions.

A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran.

Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps. Therefore, this research aims to compare the accuracy of the novel evolutionary methods of landslide susceptibility mapping. To achieve this, a unique method that integrates two techniques from Machine Learning and Neural Networks with novel geomorphological indices is used to calculate the landslide susceptibility index (LSI). The study was conducted in western Azerbaijan, Iran, where landslides are frequent. Sixteen geology, environment, and geomorphology factors were evaluated, and 160 landslide events were analyzed, with a 30:70 ratio of testing to training data. Four Support Vector Machine (SVM) algorithms and Artificial Neural Network (ANN)-MLP were tested. The study outcomes reveal that utilizing the algorithms mentioned above results in over 80% of the study area being highly sensitive to large-scale movement events. Our analysis shows that the geological parameters, slope, elevation, and rainfall all play a significant role in the occurrence of landslides in this study area. These factors obtained 100%, 75.7%, 68%, and 66.3%, respectively. The predictive performance accuracy of the models, including SVM, ANN, and ROC algorithms, was evaluated using the test and train data. The AUC for ANN and each machine learning algorithm (Simple, Kernel, Kernel Gaussian, and Kernel Sigmoid) was 0.87% and 1, respectively. The Classification Matrix algorithm and Sensitivity, Accuracy, and Specificity variables were used to assess the models' efficacy for prediction purposes. Results indicate that machine learning algorithms are more effective than other methods for evaluating areas' sensitivity to landslide hazards. The Simple SVM and Kernel Sigmoid algorithms performed well, with a performance score of one, indicating high accuracy in predicting landslide-prone areas.

Bayesian Spatial Modeling of Landslide Events Using Integrated Nested Laplace Approximation (INLA): A Study Case on Natural Conditions and Community Actions in East Java, Indonesia

Bayesian Spatial Modeling Using Integrated Nested Laplace Approximation (INLA) is an advanced statistical technique that can be used to model and analyze occurrences in geographic areas. Landslides are one of natural disasters that occur due to natural and human factors and pose a serious threat to East Java Province which has complex natural conditions. The disaster brings various losses, including economic, infrastructural, human life, and environmental. This study investigates the factors contributing to landslides across 29 districts and 9 cities in East Java, Indonesia, using spatial regression modeling by Integrated Nested Laplace Approximation (INLA). The factors include the number of seaside villages, the number of slope topography villages, and the area of temporarily uncultivated gardens and fields in 2021. The modeling results show that the number of seaside villages, the number of slope topography villages, and the area of fields that are temporarily uncultivated have a significant influence on the occurrence of landslides so that efforts to mitigate and prevent such disasters can be focused on the contributing factors. We conclude that the model might be able to identify potential landslide risk areas through mapping.

Mapping and Pre- and Post-Failure Analyses of the April 2019 Kantutani Landslide in La Paz, Bolivia, Using Synthetic Aperture Radar Data

Urban landslides have brought challenges to developing countries undergoing urbanization. Rapid approaches to assess ground deformation are required when facing the challenge of insufficient geological survey methods. Additionally, it is indeed a challenge to map landslide-affected areas, especially precipitation-induced landslides, through optical remote sensing methods. This study applied SAR change detection methods to map the slope failure event of the San Jorge Kantutani landfill site in La Paz, Bolivia, which occurred in April 2019, and Multi-Temporal Synthetic Aperture Radar Interferometry (MTInSAR) methods to assess pre- and post-failure ground stability related to this event. We found that the amplitude information of high-resolution COSMO-SkyMed SAR imagery and its texture information can be very useful in landslide mapping, especially in situations in which optical images are not available because of complex meteorological conditions and the similar spectral characteristics between the original land cover and landslide deposits. The MTInSAR analyses found that there was already significant deformation of more than 50 mm/year along the slope direction over this site before the landslide, and such deformation could be clearly discriminated from the surrounding environment. After the landslide event and the remobilization of the landslide deposit, the slope still shows a deformation velocity of more than 30 mm/year. The SAR amplitude change detection and MTInSAR fully exploited the SAR data in landslide studies and were useful in back analyzing the occurred landslides; this could be a good method for monitoring the ground stability of La Paz or even on a national scale over the long term for reducing the catastrophic effects of geological hazards in this landslide-prone city.

The effect of tree growth disturbances inertia on dendrogeomorphic spatio-temporal analysis of landslides: A case study

Tree-ring analysis is currently a frequently used absolute dating method for spatio-temporal analysis of landslide movements. However, like all absolute dating methods, this approach has various limitations. One possible limiting factor is the inertia of tree growth responses to the influence of landslide movements. When landslide movements occur with a small recurrence interval (months or early years), trees with an inertial response to an older event may not be able to record a younger event. One possible growth response of trees to a landslide movement with the absence of inertia is a change in the size of the vessel lumen area (VLA; anatomical response) in deciduous trees. Thus, this study aimed to quantify the effect of the absence of VLA inertia on the spatio-temporal analysis of two landslide events of known occurrence with a short time between their occurrence (2010 and 2013). Ninety-six individuals of European beech (Fagus sylvatica L.) were sampled and two types of landslide signals (changes in tree-ring eccentricity and changes in VLA size) were extracted from their tree-ring series. The effect of the absence of inertia was evident in the younger event, with 171.2% more trees responding anatomically than through tree-ring eccentricity. In the case of the spatial extent analysis of the younger landslide event, 29.0% more was detected as an active area through anatomical changes (VLA) compared to the tree-ring eccentricity analysis. The use of growth disturbances without inertia could in the future reduce the negative general property of tree-ring-based chronologies of landslide movements, which are considered minimal when using classical growth disturbances with inertia. The implementation of anatomical responses could lead to a more effective use of dendrogeomorphic data in landslide hazard assessment.

DEVELOPMENT OF CUMULATIVE RAINFALL THRESHOLD FOR LANDSLIDE OCCURRENCE IN PENINSULAR MALAYSIA

Significant issues related to landslides are exposed tremendously in Peninsular Malaysia which have an impact on human beings, animals as well as properties. Reported over twenty-eight significant landslides taking place between 1993 and 2011 which resulted in more than 100 deaths in total. Most of the landslides are the consequences of accumulation of water in underground soil which is connected to rainfall threshold. To establish an empirical Cumulative-Duration threshold through linear regression, analysis of 69 landslide incidents undertaken as well as rainfall data sourced from Public Works Department (PWD) and Department of Irrigation and Drainage (DID) were collected. A comprehensive assessment of all gathered parameters conducted to achieve the confidential purpose of this research which is to determine the threshold for cumulative rainfall event duration which can be utilized in early warning systems and planning for future safety measures. Thus, correlation between rainfall patterns and landslide events are observed. Cumulative rainfall threshold produced an equation E = with identical range of event duration 1 < D < 2448 h which acts as a critical line of landslide occurrences. Validation of threshold was revised using recent landslide cases to acquire new threshold values to represent current rainfall induced landslides. The threshold serves as an early warning mechanism and planning to protect lives and property.

Geomorphic understanding of loess landslides activity on the loess tableland:A case study in the Bailu Tableland, China

Loess landslides, predominantly triggered by human activity and precipitation, dominate the geomorphological evolution of extensive regions of the Chinese Loess Plateau. Tablelands constitute a large proportion of this Loess Plateau. Although the area has a long history of human occupation, the Bailu Tableland is relatively poorly studied. Recent landslide events along the edge of the tableland significantly affected lives and livelihoods. To develop a better understanding of geomorphological evolution and develop appropriate hazard management and mitigation strategies, we characterized recent landslide activity and determined whether there are discernable trends in activity. We used Unmanned Aerial Vehicle (UAV) observations, Interferometric Synthetic Aperture Radar (InSAR) monitoring, and Digital Terrain Analysis (DTA) to observe significant differences in the position, shape, and magnitude between old and new landslides. The geomorphological evolution of the tableland was initially driven by large landslides, while current and future morphological development is more likely affected by small landslide events. We demonstrate the successful application of InSAR to investigate potential landslides in well-vegetated loess landscapes and show that loess landslides tend to develop on slopes with high relative relief and high topographic wetness associated with gully erosion. Unlike landslides induced by groundwater variations in the Heifangtai and South Jingyang tablelands, precipitation and infrastructure construction form the main controls on landslide activity in the Bailu Tableland. We propose two geomorphological evolution models that describe loess tableland recession caused by landslides. The results from this research contribute to a better understanding of the reactivation behavior of landslides along loess tablelands, provide a scientific foundation for landslide hazard and risk management, and supplement understanding of the geomorphological evolution of loess tableland landscapes.

A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping

AbstractLandslides pose a significant threat to human life and infrastructure, underscoring the ongoing need for accurate landslide susceptibility mapping (LSM) to effectively assess risks. This study introduces an innovative approach that leverages multi-objective evolutionary fuzzy algorithms for landslide modeling in Khalkhal town, Iran. Two algorithms, namely the non-dominated sorting genetic algorithm II (NSGA-II) and the evolutionary non-dominated radial slots-based algorithm (ENORA), were employed to optimize Gaussian fuzzy rules. By utilizing 15 landslide conditioning factors (aspect, altitude, distance from the fault, soil, slope, lithology, rainfall, distance from the road, the normalized difference vegetation index (NDVI), land cover, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and distance from the river) and historical landslide events (153 landslide locations), we randomly partitioned the input data into training (70%) and validation (30%) sets. The training set determined the weight of conditioning factor classes using the frequency ratio (FR) approach. These weights were then used as inputs for the NSGA-II and ENORA algorithms to generate an LSM. The NSGA-II algorithm achieved a root-mean-square error (RMSE) of 0.25 during training and 0.43 during validation. Similarly, the ENORA algorithm demonstrated an RMSE of 0.28 in training and 0.48 in validation. The findings revealed that the LSM created by the NSGA-II algorithm exhibited superior predictive capabilities (area under the receiver operating characteristic curve (AUC) = 0.867) compared to the ENORA algorithm (AUC = 0.844). Additionally, a particle swarm optimization (PSO) algorithm was employed to determine the importance of conditioning factors, identifying lithology, land cover, and altitude as the most influential factors.

Landslide Prediction in Mountainous Terrain Using Remote Sensing and GIS: A Case Study of Al-Hada Road, Makkah Province, Saudi Arabia

In Saudi Arabia’s mountainous regions, debris flow is a natural hazard that poses a threat to infrastructure and human lives. To assess the potential zones of landslide in the Al-Hada Road area, a study was conducted using Geographic Information System (GIS) analysis and remote sensing (RS) data. The study took into account various factors that could affect landslides, such as drainage density, elevation, slope, precipitation, land use, geology, soil, and aspect. The study also included a field trip to identify 11 previous landslide events that were influenced by high-density drainage and slope. The study utilized weighted overlay analysis in a GIS environment to create a map indicating the potential landslide zones in the area. According to the analysis, low-risk areas cover 35,354,062.5 square meters, medium-risk areas cover 60,646,250 square meters, and high-risk zones cover an area of 8,633,281 square meters. This result was verified using the locations of previous landslide events. The study’s findings can help planners and decision-makers identify and prioritize areas for mitigation and prevention measures. Additionally, the study contributes to understanding landslide hazards in arid and semi-arid regions.

Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam

Abstract Normalized difference vegetation index (NDVI) is a conditioning factor that significantly affects slope stabilization, as the low vegetation coverage can create conducive conditions for landslide occurrence. In previous studies, NDVI was often calculated from long-term average NDVI maps or specific yearly NDVI maps. However, this approach is unsuitable due to the time-varying nature of these data, influenced by numerous factors, including human activities. To solve this problem, this study uses NDVI as a time-varying factor. NDVI maps are generated from Sentinel 2 and Landsat_8 imagery at the onset of each rainy season between 2015 and 2020 in the mountainous region of Quang Ngai Province. Moreover, the landslide events that occurred within this 5-year period (2016–2020), along with a set of conditioning factors, are utilized to develop landslide susceptibility models based on three algorithms: logistic regression, support vector machine, and extreme gradient boosting (XGBoost). The obtained results demonstrate that using time-varying NDVI shows superior performance compared to using only NDVI from 2015. The outcomes also indicate that XGBoost is the most effective model. Selecting suitable NDVI maps can improve the predictive accuracy of landslide susceptibility mapping.

Landslide Movement of Bendungan District Trenggalek Using an Artificial Neural Network

Landslide is one of the disasters that often occurs in Indonesia in the East Java Province, especially in Bendungan District, Trenggalek Regency. Analysis of landslide susceptibility in Bendungan District is needed to spatially locate the landslide occurrences. The purpose of this study was to predict landslide events using an artificial neural network. Rainfall, topography, physical soil properties, and land-use were used as the explanatory variables. An analytic hierarchy process approach was applied to determine the weight of the variables. The model satisfactorily classified the landslide hazards with an area under curve of 0.96. The northwest area of the Bendungan District was found to be a region at high risk with rainfall and soil texture as the most influential parts in triggering the landslides.

Study on landslide susceptibility mapping with different factor screening methods and random forest models.

The number of input factors affects the prediction accuracy of a model. Factor screening plays an important role as the starting point for data input. The aim of this study is to explore the influence of different factor screening methods on the prediction results. Taking the 2014 landslide inventory of Jingdong County as an example, a landslide database was constructed based on 136 landslide events and 11 selected factors, which were randomly divided into a training dataset and a test dataset according to a ratio of 7:3. Four factor screening methods, namely, the information gain ratio (IGR), GeoDetector, Pearson correlation coefficient and multicollinearity test (MT), were selected to screen the factors. A random forest (RF) model was then used in combination with each factor set for landslide susceptibility mapping (LSM). Finally, accuracy validation was performed using confusion matrices and ROC curves. The results show that factor screening is beneficial in improving the accuracy of the resulting model compared to the original model. Second, the IGR_RF model had the highest AUC value (0.9334), which was higher than that of the MT_RF model without factor screening (0.9194), and the IGR_RF model predicted the most landslides in the very high susceptibility zone (51.22%), indicating the good prediction performance of the IGR_RF model. Finally, the factor weighting analysis revealed that NDVI, elevation and aspect had the greatest influence on landslides in Jingdong County and that curvature had the least influence on landslides. This study can provide a reference for factor screening in LSM.

Influence of altitude, slope, and waterway characteristics on the occurrence of slow-moving landslides in South Korea

Forest soil sediment disasters, categorized as landslides, slow-moving landslides (land creep), and debris flows, result in destruction of forests and properties, loss of life, and damage to infrastructures. Therefore, it is imperative to evaluate factors that cause slow-moving landslides and establish a national-level hazard information system. In this study, we used geographic information system (GIS) to investigate the difference between landslides and slow-moving landslides by overlapping and analyzing landslide hazard map with slow-moving landslide sites. Slow-moving landslide areas (SLAs) and non-SLAs were characterized to compare the effects of altitude and slope in slow-moving landslide events. Finally, we performed a hydrographical analysis of waterways to compare the SLAs and non-SLAs. Grade 3 slow-moving landslide sites were found to be the most prevalent among all landslide hazard grades (1–5), including those in regions outside the specified grades. The altitude and slope of SLAs were 5 m higher and 1° steeper than those of non-SLAs, respectively. The waterways in non-SLAs (205.4 m) were longer than those in SLAs (85.2 m). Our study highlights the importance of altitude, slope, and waterways as triggers for slow-moving landslides and provides an empirical basis for establishing national hazard information systems for these events.

Assessing the risk of slope failure to highway infrastructure using automated time-lapse electrical resistivity tomography monitoring

Electrical resistivity tomography (ERT) monitoring provides time-lapse images of the subsurface. These images can be used to assess spatiotemporal variation in moisture content, which is a key driver of slope failure, making ERT monitoring an effective tool to evaluate precursory conditions of failure. This work presents the results of ERT monitoring on a slope above a major highway located on the border between England and Wales. During highway construction in the 1960s the slope was subject to several large landslide events which resulted in the re-design of the carriageway and installation of engineered mitigation measures. A section of the slope known as the ‘partially slipped area’ exhibited partial displacement during this time but did not progress to full slope failure, and therefore presents an ongoing risk to the highway, even though it does not experience ongoing displacement. An ERT monitoring system was installed across this area to monitor subsurface variations in moisture content. The results show a complex pattern of subsurface moisture dynamics within the partially slipped area when compared to the adjacent area of stable slope. This is most likely a result of the uneven and hummocky terrain in the partially slipped area and its effects on rainfall infiltration, storage and drainage, combined with the displacement-induced jointing present in the underlying sandstone units. The ERT results are used to assess the volume of unstable ground, placing the volume at the upper end of estimates from previous studies. Furthermore, analysis of the ERT dataset for surface displacements shows no movement at the site, which is confirmed by analysis of differential LiDAR plots and ground motion data derived from InSAR. This study demonstrates the application of ERT monitoring on a low activity, high risk slope, highlighting the need to understand subsurface processes at the slope-scale to inform long-term slope management.

Mapping landslide events at Cameron Highlands, Malaysia in relation to land use, rainfall and elevation using Geographical Information System

The Cameron Highlands as the name implies is subjected to high landslide vulnerability due to its geographical location in a hilly area. The principal factors contributing to the landslide events in Cameron Highlands have been identified as heavy rainfall and substantial land use changes, which include infrastructural development and agricultural practices. Thus, there is a need to map the landslide events that occurred in Cameron Highlands using Geographical Information Systems concerning rainfall, land use and elevation. The relevant data were collected from the Department of Irrigation and Drainage Malaysia, the Department of Town and Country Planning Malaysia, and the Public Works Department Malaysia. Based on the findings, with the average annual rainfall exceeding 2,687mm, massive agricultural activities on hilly terrain and infrastructure development along the main road, confirmed the occurrence of landslide events in Cameron Highlands. In addition, most of the landslide events occur at an elevation of more than 1,800m above sea level. By knowing the respective locations of previous landslide events, local authorities will be able to take earlier precautions in mitigating and planning future development related to agricultural activities and infrastructure besides providing early warning and useful information to the public at large.