Rainfall-triggered Landslides Research Articles

Data-Driven Landslide Nowcasting at the Global Scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

A Modelling Tool for Rainfall-triggered Landslide Susceptibility Mapping and Hazard Warning based on GIS and Machine Learning

The landslide susceptibility mapping and hazard warning are widely adopted tools by the government, stakeholders and the public for landslide disaster preparedness and emergency planning. This study presented a modelling tool based on geographic information system (GIS) and machine learning to aid the two-step modelling procedure. The machine learning methods including artificial neural networks, support vector machines, and logistic regression were integrated into the GIS environment for modelling landslide susceptibility to simplify and automate the routines of model training, verification and prediction. Then, the meta-element model was employed to take the landslide susceptibility, antecedent effective rainfall and 24-hour forecasted rainfall as inputs to determine the landslide hazard level. The architecture to deploy the established meta-element model for real-time landslide hazard warning was also proposed. A study case in Chunan, China was selected to demonstrate the applicability of the modelling tool to aid landslide susceptibility mapping and real-time hazard warning in response to a typhoon event. The developed modelling tool was desired to evolve into cloud computing architecture to facilitate easy-to-reuse and uplift its scalability.

Implications of terrain resolution on modeling rainfall-triggered landslides using a TIN- based model

This study employs a distributed eco-hydrological-landslide model, the tRIBS-VEGGIE-Landslide, to evaluate the influence of terrain resolution on the hydro-geomorphological processes involved in slope stability analysis. The model implements a Triangulated Irregular Network (TIN) to describe the topography starting from a grid-DEM. Five grid-DEM resolutions of the case study basin, i.e., 10, 20, 30 and 70 m, are used to derive the corresponding TINs. The results show that using irregular meshes reduces the loss of accuracy with coarser resolutions in the derived slope distribution in comparison to slope distributions estimated from the original grid-based DEM. From a hydrological perspective, the impact of resolution on soil moisture patterns and on slope stability is significant mostly when lateral water exchanges are allowed. The degrading of resolution leads to a reduction of the predicted unstable areas, with respect to the highest resolution case, from about 15% (20 m) to more than 40% (70 m).

Robust design of self-starting drains using Random Forest

Groundwater lowering is one of the most important countermeasures to avoid the risk of rainfall-triggered landslides. However, the long-term reliability of many drainage methods is often a matter of concern since the drains may easily get clogged. A new hydraulic-driven self-starting drainage method is presented in this paper. In the proposed Random Forest (RF) based robust design approach for the self-starting drains, the datasets are generated using an automatically controlled numerical modeling technology. The deterministic analysis is carried out based on uncertain soil parameters and the specific designs selected using Uniform Design (UD). The ensemble of RF models is applied in the design process to improve computing efficiency. Safety requirements, design robustness, and cost efficiency are simultaneously considered utilizing multi-objective optimization. A straightforward and efficient framework that focuses on difficulties caused by an enormous design space is established for the robust design of the self-starting drains, and improved computation efficiency is achieved. The effectiveness of the proposed approach is illustrated with a case study, the Qili landslide in Zhejiang Province, China.

Identifying landslides from continuous seismic surface waves: a case study of multiple small-scale landslides triggered by Typhoon Talas, 2011

SUMMARY Landslides can cause devastating damage. In particular, heavy rainfall-triggered landslides pose a chain of natural hazards. However, such events are often difficult to detect, leaving the physical processes poorly understood. Here we apply a novel surface-wave detector to detect and locate landslides during the transit of Typhoon Talas 2011. We identify multiple landslides triggered by Typhoon Talas, including a landslide in the Tenryu Ward, Shizuoka prefecture, Japan, $\sim 400\, {\rm km}$ east from the typhoon track. The Tenryu landslide displaced a total volume of $1.2\!-\!1.5 \times 10^{6}\, {\rm m}^3$ . The landslide is much smaller than those detected by using globally recorded surface waves, yet the event generated coherent seismic signals propagating up to 3000 km away. Our observations show that attributes of small and large landslides may follow the same empirical scaling relationships, indicating possible invariant failure mechanisms. Our results also suggest an alerting technology to detect and locate landslides with a sparse seismic network.

Application of Flow Velocity and Direction Measurement System in Slope Stability Analysis

Hydrodynamic pressure is often a crucial factor in the evaluation of slope stability analysis, especially for many rainfall-triggered landslides. Nevertheless, hydrodynamic pressure is rarely considered in the traditional limit equilibrium slice method of slope stability analysis since effective and reliable hydrodynamic pressure data are often lacking in practice. Moreover, efficient methods to involve these data in slope stability analysis are an urgent need. To overcome these concerns, the flow velocity and direction system (FVDS) is employed to measure the groundwater flow velocity, which can be used to generate hydrodynamic pressure samples at different monitoring points. Based on these samples, the hydrodynamic pressure of each soil strip is estimated using artificial neural networks (ANNs). Afterward, an improved Bishop method that considers hydrodynamic pressure is proposed. The effectiveness and significance of the proposed method are illustrated with a case study, the Fanshantou landslide in Zhejiang Province, China. The safety factor before and after taking drainage countermeasures is also calculated and compared. The results indicate that hydrodynamic pressure plays an important role in the stability analysis of the Fanshantou landslide. Compared with the classical Bishop method, the improved method is shown to agree better with the actual deformation characteristics of the landslide.

Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification

Landslides are one of the most devastating natural hazards worldwide. Landslides are triggered by different forces, such as earthquakes and typhoons, and have different characteristics in terms of distribution, influential factors, and process. The objectives of this study are to develop susceptibility maps using machine learning for two different triggering forces (earthquake and typhoon) and identify the main predisposing factors in mountainous regions of Pakistan and Taiwan. To compare different machine learning models for landslide susceptibility mapping, landslide susceptibility maps were developed using traditional (logistic regression) and modern techniques (decision tree). Results show that the spatial pattern of susceptibility map from logistic regression is continuously distributed, whereas that from the decision tree is crisp and sharp. From both models, consistent results show that the most important critical factors are completely different for both the earthquake- and typhoon-triggered landslides. For rainfall-triggered landslides in Taiwan, the most important factor of landslide susceptibility is the distance to the rivers, whereas, for earthquake-triggered landslides in Pakistan, the most important one is geological formations. Moreover, landslide susceptibility maps show that earthquake-triggered landslides tend to occur at the Muzaffarabad Formation, whereas rainstorm-induced landslides aggregate in the slope toe along the river.

A Statistical Model for Earthquake And/Or Rainfall Triggered Landslides

Natural hazards can be initiated by different types of triggering events. For landslides, the triggering events are predominantly earthquakes and rainfall. However, risk analysis commonly focuses on a single mechanism, without considering possible interactions between the primary triggering events. Spatial modeling of landslide susceptibility (suppressing temporal dependence), or tailoring models to specific areas and events are not sufficient to understand the risk produced by interacting causes. More elaborate models with interactions, capable of capturing direct or indirect triggering of secondary hazards, are required. By discretising space, we create a daily-spatio-temporal hazard model to evaluate the relative and combined effects on landslide triggering due to earthquakes and rainfall. A case study on the Italian region of Emilia-Romagna is presented, which suggests these triggering effects are best modeled as additive. This paper demonstrates how point processes can be used to model the triggering influence of multiple factors in a large real dataset collected from various sources.

Using Google Earth Images to Extract Dense Landslides Induced by Historical Earthquakes at the Southwest of Ordos, China

As historical earthquake records are simple, determining the source parameters of historical strong earthquakes over an extended period is difficult. There are numerous uncertainties in the study of historical earthquakes based on limited literature records. Co-seismic landslide interpretation combined with historical documents can yield the possibility of reducing these uncertainties. The dense co-seismic landslides can be preserved for hundreds to thousands of years in Loess Plateau, North China; furthermore, there are notable attribute differences between earthquake landslides and rainfall-triggered landslides. Along the southwestern margin of the Ordos Block, only one severe earthquake has been recorded in the past 3,000 years. The records of “Sanchuan exhaustion and Qishan collapse” provide clues for an investigation of the 780 BC Qishan earthquake. In this study, combined with historical documents, current high-resolution Google Earth images were used to extract historical landslides along the southwestern of the Ordos Block. There were 6,876 landslides with a total area of 643 km2. The landslide-intensive areas were mainly distributed along the Longxian–Qishan–Mazhao Fault in the loess valley area on the northeastern side of the fault. Loess tableland and river terraces occur on the southwest side of the fault; dense landslides have not been examined due to the topographical conditions in this area. By analyzing the spatial distribution of historical earthquake damage in this region, comparing the characteristics of rainfall-triggered landslides, and combining existing dating results for bedrock collapse and loess landslides, the interpretation of dense historical landslides can be linked to the Qishan Earthquake. The interpretation results are associated with historical records. Analyses of current earthquake cases show that the distribution of dense landslides triggered by strong earthquakes can indicate the episeismic area of an earthquake. In addition, the non-integrated landslide catalog without small- and medium-scale coseismic landslides can be used to effectively determine the source parameters of historical strong earthquakes and perform quantitative evaluations. This study evaluates the focal parameters of the 780 BC Qishan earthquake based on interpretations of the spatial distribution range of historical landslides as representations of the range of the extreme earthquake zone.

GIS-Based Landslide Susceptibility Map Verification by its Geotechnical and Geological Characteristics

The famous 2006 Laplae rainfall-triggered landslide has caused hundreds of million Baht lost. The objective of this paper is to evaluate and to compare present geotechnical and geological conditions between the landslide area and landslide-free area to verify a landslide susceptibility map. Detailed field surveys acquired 14 samples: undisturbed, disturbed soils as well as rocks. Sensitivity and back-analysis were also performed. 35.44% of landslides occurred in meta-shale - greywacke-interbedding Formation, and another 64.56% happened in meta-greywacke - basaltic andesite Formation. The dominant slope of the landslide is in 300-400 slope, with aspects toward South direction, and in a range of <1,000 meters from geological structures. Landslide area is distinguished from the non-landslide area by its various grades of materials, lower quartz content, higher clay content, finer composition of parental rocks and soils, higher average ratio of smectite versus quartz, lower compressive strength, lower shear strength, lower angle of internal friction, lower cohesion value, more poorly graded, higher soil moisture, higher liquid limit and plasticity index, and factor of safety is generally less than 1. The result of the sensitivity analysis indicated that in a failure condition, the cohesion and angle of internal friction angle values are equal to 2.51 kPa and 13.700, respectively. However, the results of laboratory tests showed that the cohesion and angle of internal friction values are equal to 8.59 kPa and 14.20. These results verified the landslide susceptibility map that the high landslide susceptibility area has the engineering characteristics of the landslide-prone area, while the low susceptible one has the characteristics of the non-landslide area.

Including informal housing in slope stability analysis – an application to a data-scarce location in the humid tropics

Abstract. Empirical evidence from the humid tropics shows that informal housing can increase the occurrence of rainfall-triggered landslides. However, informal housing is rarely accounted for in landslide hazard assessments at community or larger scales. We include informal-housing influences (vegetation removal, slope cutting, house loading, and point water sources) in a slope stability analysis. We extend the mechanistic model CHASM (Combined Hydrology and Stability Model) to include leaking pipes, septic tanks, and roof gutters. We apply this extended model (CHASM+) in a region of the humid tropics using a stochastic framework to account for uncertainties related to model parameters and drivers (including climate change). We find slope cutting to be the most detrimental construction activity for slope stability, and we quantify its influence and that of other destabilising factors. When informal housing is present, more failures (+85 %) are observed in slopes that would otherwise have had low landslide susceptibility and for high-intensity, short-duration precipitations. As a result, the rainfall threshold for triggering landslides is lower when compared to non-urbanised slopes and comparable to those found empirically for similar urbanised regions. Finally, low cost-effective “low regrets” mitigation actions are suggested to tackle the main landslide drivers identified in the study area. The proposed methodology and rainfall threshold calculation are suitable for data-scarce contexts, i.e. when limited field measurements or landslide inventories are available.

Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan

Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change.

Critical Continuous Rainfall Map for Forecasting Shallow Landslide Initiations in Busan, Korea

In recent years, precipitation patterns in Korea have shifted to be characterized as short and intense rainfalls. In consideration of shallow landslide initiations primarily governed by heavy rainfalls at short-time scales that diminish drainage effects, the concept of critical continuous rainfall is proposed as a single-rainfall-variable threshold for shallow landslide forecasting. To generate a critical continuous rainfall map for hillslope areas in a city of Korea (Busan), this study designed and applied a systematic modeling process. As a preparatory stage, input datasets of geo-hydraulic properties and geotechnical properties were assembled using estimation techniques based on experiment data of field samples. The inherent and fixed critical continuous rainfall values for hillslope areas in Busan were derived through one-dimensional infiltration analysis coupled with infinite slope stability calculations. As a result of a detailed analysis of historical rainfall records in a case study area over a period of 11 years, three false forecasting cases were recorded, whereas all landslide-triggering rainfall events were correctly captured with no missed forecasting cases. The results of the case study indicate that the proposed critical continuous rainfall may be useful as an effective and straightforward indicator for forecasting the initiation of shallow landslides.

Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea

The intensity-duration (I-D) threshold is considered an effective indicator for landslides triggered by short-term high-intensity rainfall and long-term low-intensity rainfall. However, previous studies have not considered the influence of antecedent rainfall. Herein, we analyzed hourly rainfall data for 613 shallow landslides that occurred from 1963 to 2018 in South Korea to obtain rainfall thresholds and investigated the effect of antecedent rainfall conditions on threshold variations. The I-D and IMAP-D, which is normalized by dividing I by mean annual precipitation (MAP), thresholds were determined to be I = 10.40D–0.31 and IMAP = 0.006D–0.26 (4 ≤ D (h) ≤ 84), respectively, at the 2nd percentile level through quantile regression analysis. These thresholds were lower than other local thresholds (i.e., excluding global and regional thresholds), suggesting that the southern region of the Korean Peninsula was more susceptible to rainfall-induced landslides. Although the effective length of antecedent rainfall was not presented herein, the I-D threshold of landslide-triggering rainfall was confirmed to be low for the absolute and/or calibrated antecedent rainfalls greater than event cumulative rainfall at 5, 7, 10, and 20 days prior to the event. Such differences in I can be greater at shorter durations; however, these differences gradually decrease as D increases, suggesting that they lose their effect as soil water content increases. The results of the current study can improve the understanding of the effect of antecedent rainfall conditions on landslide occurrence and should be further tested with respect to the hydrologic response of hillslopes by considering regional climate and local site conditions.

Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution

As the frequency and intensity of heavy rainfall increase, the frequency of extreme rainfall-induced landslides also increases. Thus, the importance of accurate assessment of extreme rainfall-induced landslide hazard increases. Landslide hazard assessment requires estimations of two components: spatial probability and temporal probability. While various approaches have been successfully used to estimate spatial landslide susceptibility, fewer studies have addressed temporal probability and, consequently, a commonly accepted method does not exist. Prior approaches have estimated temporal probability using frequency analysis of past landslides or landslide triggering rainfall events. Hence, a large amount of information was required: sufficiently complete historical data on recurrent landslides and repetitive rainfall events. However, in many cases, it is difficult to obtain such complete historical data. Therefore, this study developed a new approach that can be applied to an area where incomplete data are available or where nonrepetitive landslide events have occurred. To evaluate the temporal probability of landslide occurrence, the developed approach adopted extreme value analysis using the Gumbel distribution. The exceedance probability of a rainfall threshold was evaluated, using the Gumbel model, with 72-h antecedent rainfall threshold. This probability was then considered to be the temporal probability of landslide occurrence. The temporal probability of landslides was then integrated with landslide susceptibility results from a multi-layer perceptron model. Consequently, the landslide hazards for different future time periods, from 1 to 200 years, were estimated.

Determination of Empirical Rainfall Thresholds for Shallow Landslides in Slovenia Using an Automatic Tool

Rainfall-triggered shallow landslides represent a major threat to people and infrastructure worldwide. Predicting the possibility of a landslide occurrence accurately means understanding the trigger mechanisms adequately. Rainfall is the main cause of slope failures in Slovenia, and rainfall thresholds are among the most-used tools to predict the possible occurrence of rainfall-triggered landslides. The recent validation of the prototype landslide early system in Slovenia highlighted the need to define new reliable rainfall thresholds. In this study, several empirical thresholds are determined using an automatic tool. The thresholds are represented by a power law curve that links the cumulated event rainfall (E, in mm) with the duration of the rainfall event (D, in h). By eliminating all subjective criteria thanks to the automated calculation, thresholds at diverse non-exceedance probabilities are defined and validated, and the uncertainties associated with their parameters are estimated. Additional thresholds are also calculated for two different environmental classifications. The first classification is based on mean annual rainfall (MAR) with the national territory divided into three classes. The area with the highest MAR has the highest thresholds, which indicates a likely adaptation of the landscape to higher amounts of rainfall. The second classification is based on four lithological units. Two-thirds of the considered landslides occur in the unit of any type of clastic sedimentary rocks, which proves an influence of the lithology on the occurrence of shallow landslides. Sedimentary rocks that are prone to weathering have the lowest thresholds, while magmatic and metamorphic rocks have the highest thresholds. Thresholds obtained for both classifications are far less reliable due to the low number of empirical points and can only be used as indicators of rainfall conditions for each of the classes. Finally, the new national thresholds for Slovenia are also compared with other regional, national, and global thresholds. The thresholds can be used to define probabilistic schemes aiming at the operative prediction of rainfall-induced shallow landslides in Slovenia, in the framework of the Slovenian prototype early warning system.

Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach

Rainfall-triggered landslides are the most common type of mass movement seen along the tropical belt due to the prevalence of monsoons. These landslides can be forecasted by understanding the spatial and temporal rainfall distribution patterns, and subsequent generation of rainfall threshold (RT). However, deriving a regional RT in a geologically, geographically and physiographically diverse milieu is a formidable task. The data on spatial and intra-seasonal variability of monsoons can be widely dispersed in such diversified terrains. Clustering analysis provides a promising approach to handle such widely dispersed data. This study intends to develop a methodology using 2-stage clustering process to create RT in such terrains by using daily rainfall versus antecedent rainfall and rainfall versus antecedent rainfall versus soil depth. Sixteen rainfall-induced landslides, located in different terrains in the Western Ghats of India, were subjected to this analysis. Majority of the landslides were modeled, and different RTs were derived for different conditions. The landslides belong to four different classes, viz., landslides occurring at steep slopes; those occurring at knickpoints of highland and midland; in the plateau region and others characterized by a thin veneer of soil. Out of 16 landslides subjected to RT, this method was able to model 13 landslides with a success rate of 81.25%, which is a fair figure.

Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China

The Upper Minjiang catchment is located at the eastern margin of the Tibetan Plateau and is frequently affected by landslides. The two main triggering factors in the region are rainfall and high magnitude seismic shocks, such as the 2008 Wenchuan earthquake. In this paper, we present a comparison study of pre-conditioning factors that drive the spatial occurrence of landslides in this high elevation region. We used a multi-temporal landslide inventory that differentiates between rainfall and earthquake triggers based on the analyses of satellite imagery, aerial photos, and field investigations. Our investigation revealed that the strongest influences on landslides are lithology and topographic factors. The difference between these two triggering factors can also be observed with respect to the relative slope positions: rainfall-triggered landslides are more frequently found at lower slopes, while seismic-induced landslides are more evenly distributed on very steep slope sections. In particular, the steep and deeply incised river terraces in this region are prone to landslides due to their rapid formation. This study presents the first analysis that differentiates two main triggering factors in order to understand the influence of rainfall and earthquakes on landslide occurrences in this region. Our results can also aid in the sustainable management of performance-based slope engineering and landslide risk reduction by reporting the differences regarding the spatial distribution of rainfall- and seismic-triggered slope failures. Consequently, these results can be used by local and regional decision-makers for risk management and spatial planning.

Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh

Since August 2017, more than 744,400 stateless Rohingya refugees – an ethnic Muslim minority group from the Rakhine State – have entered Bangladesh to escape serious crimes against humanity conducted by the Myanmar Army. Extensive level of deforestation and hill cutting activities took place in Cox’s Bazar District (CBD) in Bangladesh to accommodate them. The refugee camps are sitting on hills and loose soil and are highly vulnerable to rainfall-triggered landslides. Notably in June 2017, landslides in the same region killed at least 160 people. From this perspective, the study aims to develop a localised landslide early warning system (EWS) for the Rohingya refugees and their host communities in CBD. A novel method, combining landslide inventory and susceptibility maps, rainfall thresholds and dynamic web-based alert system, has been introduced to develop the landslide early warning system (EWS) by applying advanced geoinformation techniques. Results suggest that approximately 5,800 hectares of forest land cover disappeared due to the 2017 Rohingya influx. Land cover changes through hill cutting andslope modifications, and unplanned urbanisation are predominantly responsible for slope failures and consecutive 5-day periods of rainfall between 95–220 mm could initiate landslides in high susceptible areas. The EWS would support the local authorities and international organisations in reducing disaster risks and saving lives from landslides in a humanitarian context.

Analysis of rainfall-triggered landslide in Baleagung Village, Magelang Regency, Central Java

The majority of Magelang Regency is considered as intermediate and high susceptible areas against landslide. As happened in Baleagung Village in January 2018, a landslide has damaged local residency buildings. The landslide occurred because of high-intensity rainfall on the day before. Meanwhile, cracks formed in several areas that were indicated by the slope around the village were still actively moving. Therefore, mitigation is urgently needed to minimize the risk of further landslide movement. This study aimed to analyze the mechanism of ground movement at Baleagung Village which was affected by rainfall. This research was conducted by using back-analysis method to determine the initial groundwater level conditions and its effect on the slope stability. Soil parameters were obtained by laboratory testing and permeability tests in the field. Subsequently, rainfall data was collected from Himawari-8 satellite and used as the data input parameter for numerical analysis using SEEP/W and SLOPE/W. The results obtained from back-analysis showed that rainfall infiltration into the soil affected the degradation of safety factor value that was caused by the increase of pore-water pressure and the decrease of effective stress in a certain period. The degradation of safety factor was caused by the increasing of soil saturation degree when water infiltrated into the ground.