Rainfall-induced Landslides Research Articles

Analysis of Soil–Water Characteristics and Stability Evolution of Rainfall-Induced Landslide: A Case of the Siwan Village Landslide

This paper aimed to study the soil–water characteristics and stability evolution law of rainfall-induced landslide. Taking the two landslide events in Siwan village as an example, the formation conditions of the disaster and landslide characteristics were analyzed. Additionally, the deformation characteristics and destruction mechanisms of landslides were discussed in-depth. The soil–water characteristics and hydraulic conductivity of the landslides were analyzed based on TRIM experiment results. Geo-Studio numerical software was further used for typical sections to analyze the stability of the evolution of the landslide events under rainfall conditions. The results showed that (1) The soil–water characteristic curve (SWCC) inversely varies with water content volume, and the sliding body has lower saturated water content and matrix suction than the sliding zone. The hydraulic conductivity function (HCF) increases with water content volume, and the sliding body has higher hydraulic conductivity (0.43 m/d) than the sliding zone (0.03 m/d). (2) Rainfall is the primary cause of landslides, and there is a hysteretic effect. Heavy rainfall will inevitably accelerate the formation of landslides in the analysis of the deformation characteristics and destruction mechanisms of rainfall-induced landslides. (3) Compared with the engineering analogy of the Fredlund and Xing (FX) model, the Van Genuchten–Mualem (VGM) model of the soil–water characteristics test based on the TRIM experimental system can better reflect the actual field situation. The numerical simulation method based on the TRIM experiments of the soil–water characteristics test is scientifically sound and reliable for the stability evolution of overburden rainfall-induced landslides.

Probabilistic back analysis of rainfall-induced slope failure considering slope survival records from past rainfall events

Many rainfall-induced landslides occur on slopes that have limited, or even no, site investigation data before failure. Probabilistic back analysis of slope failure provides an effective tool to back analyze possible pre-failure soil parameters, thus gaining insights into the mechanism of slope failure. For a slope failure induced by rainfall, the actual factor of safety (FS) is time-variant when time-variant rainfall and infiltration are explicitly modeled. This study proposes a novel probabilistic back analysis method that models explicitly the rainfall triggering mechanism for a rainfall-induced slope failure. Unlike existing methods that are based on a constant FS = 1, the proposed method utilizes FS inequality information for probabilistic back analysis, including slope failure record with FS < 1 and slope survival records from past rainfall events with FS > 1. The proposed method is suitable for high-dimensional problems when soil hydraulic properties are also back analyzed. The proposed method converges to the conventional methods with FS = 1 when the time span of slope failure is narrowed down to a few hours and slope survival records are ignored. Incorporating both slope failure and survival records effectively reduces uncertainties in soil strength and hydraulic parameters.

Using the Spatiotemporal Hot Spot Analysis and Multi-Annual Landslide Inventories to Analyze the Evolution and Characteristic of Rainfall-Induced Landslide at the Subwatershed Scale in Taiwan

This study used rainfall and annual landslide data for the 2003–2017 period in Taiwan to determine the long-term evolution of landslides and conducted a spatiotemporal analysis of landslides at the subwatershed scale. The historically severe landslide induced by Typhoon Morakot in 2009 was mainly distributed in the central mountainous region and southern Taiwan. The Mann–Kendall trend test revealed that in 2003–2017, 13.2% of subwatersheds in Taiwan exhibited an upward trend of landslide evolution. Local outlier analysis results revealed that the landslide high–high cluster was concentrated in the central mountainous region and southern Taiwan. Moreover, the spatiotemporal analysis indicated 24.2% of subwatersheds in Taiwan in 2003–2017 as spatiotemporal landslide hot spots. The main patterns of spatiotemporal landslide hot spots in 2003–2017 were consecutive, intensifying, persistent, oscillating, and sporadic hot spots. The recovery rate in the first two years after the extreme rainfall-induced landslide event in Taiwan was 22.2%, and that in the third to eighth years was 31.6%. The recovery rate after extreme rainfall-induced landslides in Taiwan was higher than that after major earthquake-induced landslides in the world, and the new landslides were easily induced in the area of rivers and large landslide cases after Typhoon Morakot in 2009.

Reviews and Syntheses: Promoting the Advancement of Hillslope Hydrology and Stability in Taiwan from the Perspective of Critical Zone Science

Owing to active orogenic movement and the monsoon climate, rainfall-induced landslide disasters often occur in Taiwan. Hence, hillslope hydrology and stability have received considerable research attention. However, it remains difficult to accurately estimate the duration and consequences of hillslope instability induced by hillslope hydrology. Research on hillslope hydrology and stability is complicated by spatial heterogeneity, hydrological processes operating at various scales, spatiotemporal evolution, and geomorphological properties. Recent advances in critical zone science have provided an approach to extend geoscience studies. The “deep coupling” concept is essential for integrating physical, chemical, and biological processes on various spatiotemporal scales and for providing a macro and unified framework for evaluating internal properties and processes. Critical zone science and hillslope hydrology and stability both depend on interdisciplinary perspectives and approaches, monitoring strategies, and model analysis of integrating and coupling processes. They both share the characteristics of spatial heterogeneity, continuous evolution, and relevance to ecosystem services. To address the challenges related to hillslope hydrology and stability in Taiwan, we reviewed the progress in, relevance between, and common challenges to hillslope hydrology, stability, and critical zone science. We then presented a process-based integrated monitoring strategy, an interdisciplinary perspective, and a coupling analysis framework and model. The aim of this study was to promote the advancement of research on hillslope stability and hydrology in Taiwan.

Definition of Rainfall Thresholds for Landslides Using Unbalanced Datasets: Two Case Studies in Shaanxi Province, China

The Lueyang and Xunyang counties in the Shaanxi province (China) are highly susceptible to rainfall-induced landslides. Rainfall thresholds are the most used tool to predict the occurrence of rainfall-induced landslides over large areas. However, the definition of robust thresholds may be difficult for unbalanced datasets, for which the number of non-landslide observations is much higher than the number of landslide observations. This study aims at defining adequate rainfall thresholds for the two study areas using landslide datasets that are strongly unbalanced in terms of occurrences vs. non-occurrences. Two types of rainfall thresholds are determined using a frequentist method at several non-exceedance and exceedance probabilities, separately considering rainfall events responsible for landslides (positive thresholds) and rainfall events not responsible for landslides (negative thresholds). The comparison between the two sets of thresholds shows that the method based on non-triggering events allows defining rainfall thresholds characterized by lower uncertainties and a better performance than the ones defined considering the triggering events, in both the study areas. In particular, the best-performing thresholds are the negative threshold defined at 15% exceedance probability for the Lueyang county and the negative threshold defined at 20% exceedance probability for the Xunyang county.

Earthquake and rainfall-induced landslide hazard assessment of Kutupalong Rohingya camp using meteorological and geological information

Earthquake and rainfall-induced landslide hazard assessment of Kutupalong Rohingya camp using meteorological and geological information

Back-analysis of a rainfall-induced landslide case history using deterministic and random limit equilibrium methods

Because recurrent rainfall-induced landslide episodes mimic prior failures, this study builds a back-analysis approach to identify those prior failure events and assess the prospective slope performance going forward. Engineers use the results of this approach to project the slope performance based on the quantity of rainfall accumulation during the anticipated rainfall event. A landslide case that occurred in 2004, as a result of a typhoon event, was selected for the back-analysis procedure. Four typhon events were chosen to simulate the pore water pressure conditions, which may have caused damage of varying severity (including landslide failure) to the slope. The numerical model was initially analyzed via the deterministic limit equilibrium slope stability approach to evaluate the most critical failure surface. By adopting the Monte Carlo simulation approach, the following step assessed the random limit equilibrium slope stability on the most critical failure surfaces. The probabilistic distributions of the safety factor were then generated for each typhoon event. Subsequently, the simulation outcomes provide safety indicator values incorporating the mean safety factor, failure probability, and reliability index. From the failure probability and reliability index, a zonation approach with various site-specific performance levels were established by using the corresponding accumulated rainfall to define the exceedance threshold values for each zone. In order to confirm the site-specific performance, we compared the normalized error values acquired from the analysis technique to those measured under two recent typhoon occurrences. An acceptable consistency was attained, validating the procedure's appropriateness for future rainfall events.

Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

&lt;span lang="EN-US"&gt;In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.&lt;/span&gt;

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure — exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016–2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries.

Rainfall – Induced Landslides in Kerala, India: A General Review

Landslides are a profoundly upsetting and devastating natural catastrophe. In comparison to previous decades, landslides have been recorded to occur significantly more frequently worldwide in this decade. The landslide susceptibility map is crucial for identifying landslide hotspots in the study area, identifying different factors/parameters that affect the landslide and critically examining their involvement in landslide occurrences. The significance of rainfall patterns in initiating landslides has been widely acknowledged. In Kerala, it may be possible to understand the geographical patterns of landslide distribution by examining the hotspots for landslides that have been found using maps of landslide susceptibility. Due to the spatial issues of precipitation, the impacts were mostly concentrated along valleys in the study area's lower part. Furthermore, well-forested hillslopes were more prone to landslides, indicating that trees have a limited role in stabilising slopes during extreme rainfall events. The study is being done in response to the landslides that occurred in the state of Kerala, India, between 2018 and 2020. The review addresses the major physical causes of landslides in common and major cause of landslide in Kerala which is Rainfall - Induced landslides. The triggering and progression of rainfall-induced landslides are also reviewed in this review article. Keywords: landslide; rainfall-induced landslide; rainfall pattern; Kerala

Empirical Approach Based Rainfall Threshold Estimation for Landslide Occurrence in Cox's Bazar District, Bangladesh

Rainfall threshold estimation empirically to forecast rainfall-induced landslide events provides crucial information to reduce landslide impact. The landslide events triggered by rainfall are common in Cox's Bazar district, especially during the monsoon season. Geological settings and climatic conditions make this area more landslide-prone leading to huge losses of lives and property. Establishing an effective early warning system based on a rainfall threshold value has become a top priority to save people's lives, the economy, and the environment. We have employed three empirical approaches to estimate rainfall thresholds. Intensity-Duration, Event-Duration, and Antecedent Rainfall thresholds are the most conventional rainfall methods to identify the lowest amount of rainfall that triggers landslide. The Intensity-Duration (ID) and Event-Duration (ED) rainfall threshold equations are calculated using a simple power-law curve. For 5% exceedance probability level ID defined as: T5: I = 3.63 D-0.1313 &amp; ED as T5: E = 3.63 D0.8687. Similarly, for 1% exceedance probability level ID defined as: T1: I= 2.78 D-0.1313 &amp; T1: E = 2.78 D0.8687 for ED. Both 1% and 5% rainfall threshold equations are the minimum rainfall threshold equations. Since the 5% exceedance probability threshold line delineates the lower end of all the observed data points for landslide events, it is considered the minimum threshold line for Cox's Bazar. According to the 5% exceedance probability level threshold equation, mean intensity of 2.39 mmh-1 or 57.4 mm cumulated rainfall in 24 hours is required to initiate a landslide event. Whereas, for longer duration events such as 120h, rainfall intensity of 1.93 mmh-1 or continuous rainfall of 232 mm appears to be sufficient in landslide initiation. There is a less than 5% chance of a landslide below this threshold limit. In the context of 3-day and 5-day antecedent rainfall thresholds, we found that a minimum of 130 mm in 72 h (3-day) and 210 mm in 120 h (5-day) could initiate a landslide event. We have compared our thresholds with a few global and local rainfall threshold estimates. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 11(1), 2022: 81-94

Numerical Analysis on the Stability of Sandstone-Covered Mudstone Cutting Slopes Considering Rainfall Infiltration

Landslide caused by rainfall are easily produced when excavating conduct in sandstone-covered mudstone strata. The expansion project of the Heishan Village to Jixi section of Dan-A Highway is used as an example to study the formation and damage mechanism of rainfall-induced landslide in sandstone-covered mudstone cutting slope. Finite element numerical is used to analyze the stability of the slope by using Geo-Studio and ANSYS software for the three different rainfall-influencing factors of rainfall intensity, duration and rainfall form. The study indicates that rainfall time and intensity will decrease the stability of the cutting slope, and that the effect of rainfall on the slope is lagging behind. When rainfall intensity is 80 mm, rainfall duration is 1 d, and stagnation is 5 h, the slope instability is in the critical state. The front slope toe is the first unstable part in the process of slope instability. Rock and artificial construction are important reasons for this phenomenon.

Insight from a Physical-Based Model for the Triggering Mechanism of Loess Landslides Induced by the 2013 Tianshui Heavy Rainfall Event

Rainfall-induced landslides pose a significant threat to human life, destroy highways and railways, and cause farmland degradation in the Loess Plateau. From 19 June 2013 to 26 July 2013, continuous and heavy rainfall events occurred in the Tianshui area, Gansu Province. This strong rainfall process included four short-term serious rainfall events and long-term intermittent rainfall, triggering many shallow loess landslides. To improve our understanding of this rainfall process as the triggering mechanism of the loess landslides, we conducted the physical-based spatiotemporal prediction of rainfall-induced landslides. By utilizing precipitation data recorded every 12 h from the rain gauge stations and 51 soil samples from within a 50 km radius of the study area, we predicted 1000 physical-based model-calculated pictures of potential landslides, and the slope failure probability (Pf) of the study area was obtained by Monte Carlo simulations. The model was validated by the actual landslide data of the 2013 heavy rainfall event, and the effects of the precipitation process and the trigger mechanism on the landslides were discussed. The results showed that the fourth rainfall event had the best prediction ability, while the third event had the second-best prediction ability. There was a solid linear link between the antecedent precipitation (Pa) and the predicted landslide area (Pls) based on the fitting relationship, indicating that antecedent rainfall may play a significant role in the occurrence of landslides in the region. By comparing the distribution of the predicted results of the four heavy rainfall events with the actual landslide, we observed that the first two rainfall processes may not have been the main reason for slope failure, contributing only to prepare for the landslides in the later period. The superposition of the fourth and third rainfall events finally determined the spatial distribution characteristics of the landslide induced by the 2013 heavy rainfall event.

A free web-based approach for rainfall-induced landslide susceptibility modeling: Case study of Clearwater National Forest, Idaho, USA

This study presents an interactive web-based approach for modeling rainfall-induced landslide susceptibility using Free Open Source Software (FOSS). The design is based on the R statistical framework and Shiny package coupled with the shallow slope stability model (SHALSTAB) from SAGA GIS. The easy-to-use real-time application extends the potential of current modeling efforts to non-expert R and GIS users and can also be used in an educational context for classroom teaching activity and enabling research-informed learning. The parsimonious approach (i.e. few parameter inputs) is accomplished in two sequential steps including modeling and validation by the use of site-specific datasets. The approach was tested in a case study on the Clearwater National Forest and the results from the validation showed an overall accuracy of 0.894, kappa of 0.789 and AUC from ROC curve was 0.715. The modeled landslide potential may be used as a decision-support tool for local planning.

Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India

The number of rainfall-induced landslides and the resulting casualties are increasing worldwide. Efficient Landslide Early Warning Systems (LEWS) are the best way to reduce the risk due to such events, but the number of operational LEWS is still limited. A new data-driven approach for spatio-temporal landslide forecasting on a regional scale is proposed, integrating Landslide Susceptibility Maps (LSMs) using RF algorithm and probabilistic hydro-meteorological thresholds, considering both rainfall severity and antecedent soil wetness. The proposed method is also compared with two deterministic process-based approaches: Transient Rainfall Infiltration and Grid-based Regional Slope Stability (TRIGRS) and SHALSTAB, considering the spatial variability in soil thickness and properties, along with the rainfall data. The quantitative comparison is carried out for two test areas in the Western Ghats of India (Idukki and Wayanad), for two different spatial resolutions. The efficiency and area under the curve (AUC) values from a receiver operating characteristic curve (ROC) were used to evaluate the performance of different models. The results for Idukki indicate that the efficiency values of the data-driven approach were improved by 4.67 % by using fine resolution DEM (digital elevation model) of 12.5 m resolution, while in the case of TRIGRS and SHALSTAB models, the improvements were 3.39 % and 1.83 %, respectively. For Wayanad, the improvement in efficiencies was further lesser, 2.59 % in the case of data-driven model, and 0.95 % and 0.73 % in the cases of TRIGRS and SHALSTAB, respectively. The maximum efficiency and AUC values were obtained by the data-driven model for both regions, with a spatial resolution of 12.5 m. The maximum efficiency values were obtained as 81.21 % and 83.33 % for Idukki and Wayanad, respectively, and the corresponding AUC values were 0.92 and 0.93. The results indicate that the model proposed in this study, with data-driven approach performs better than the process-based approaches and can bypass the complexities involved in modeling.

Propagation Modeling of Rainfall-Induced Landslides: A Case Study of the Shaziba Landslide in Enshi, China

Geological disasters, especially landslides, frequently occur in Enshi County, Hubei Province, China. On 21 July 2020, a large-scale landslide occurred in Enshi due to continuous rainfall. The landslide mass blocked the Qingjiang River, formed a dammed lake and caused great damage to surrounding roads and village buildings. In this study, the geomechanical properties of the landslide mass were obtained through field surveys. A three-dimensional topography model of the slope was established using the particle flow code (PFC) and the numerical parameters of the model were calibrated. A 3D discrete element model (DEM) was used to simulate the propagation of Shaziba landslide, and the dynamic behavior of the landslide was divided into five stages. The simulation results show that the landslide movement lasted approximately 1000 s. The maximum average velocity of the landslide reached up to 7.5 m/s and the average runout distance was about 1000 m. The simulated morphology of the landslide deposits was in good agreement with the field data. In addition, the influence of effective modulus on the calculation results was analyzed. The results indicate that the propagation behavior of a landslide and the morphology of landslide deposits are closely related to the effective modulus in the contact model of the PFC3D.

Revalidation Technique on Landslide Susceptibility Modelling: An Approach to Local Level Disaster Risk Management in Kuala Lumpur, Malaysia

Landslide susceptibility modelling in tropical climates is hindered by incomplete inventory due to rapid development and natural processes that obliterate field evidence, making validation a challenge. Susceptibility modelling was conducted in Kuala Lumpur, Malaysia using a new spatial partitioning technique for cross-validation. This involved a series of two alternating east-west linear zones, where the first zone served as the training dataset and the second zone was the test dataset, and vice versa. The results show that the susceptibility models have good compatibility with the selected landslide conditioning factors and high predictive accuracy. The model with the highest area under curve (AUC) values (SRC = 0.92, PRC = 0.90) was submitted to the City Council of Kuala Lumpur for land use planning and development control. Rainfall-induced landslides are prominent within the study area, especially during the monsoon period. An extreme rainfall event in December 2021 that triggered 122 landslides provided an opportunity to conduct retrospective validation of the model; the high predictive capability (AUC of PRC = 0.93) was reaffirmed. The findings proved that retrospective validation is vital for landslide susceptibility modelling, especially where the inventory is not of the best quality. This is to encourage wider usage and acceptance among end users, especially decision-makers in cities, to support disaster risk management in a changing climate.

An overview of different models used in the deterministic approach for landslide susceptibility mapping

Landslides, which can cause fatalities, property damage, and economic disruption, are some of the most catastrophic natural disasters in hilly areas. Researchers have created landslide susceptibility maps to forecast rainfall-induced landslides, particularly in hill site developments. A physically based model is more familiar in rainfall-induced landslide analysis, which consists of hydrological and infinite slopestability models. This paper discusses the susceptibility of rainfall-induced landslides through four approaches: shallow landslide stability model, stability index mapping, transient rainfall infiltration, gridbased regional slope-stability model, and Yon-Sey slope model. The basic concepts, applications and limitations of each model are highlighted. SINMAP and SHALTAB do not consider the infiltration and groundwater flow temporally compared to TRIGRS and YS models. Nonetheless, TRIGRS solely considers 1-dimensional infiltration versus time without spatially compromising groundwater flow. Besides, YS-Slope considers regional and temporal distributions of fluctuations in groundwater variations, which can predict shallow and deep-seated failure. Further exploration needs to be carried out due to the limitations of applying these four models, which are applicable to simple landslide processes in relatively homogenous and suitable geomorphology and geology conditions.

The spatial distribution of debris flows in relation to observed rainfall anomalies: Insights from the Dolan Fire, California

A range of hydrologic responses can be observed in steep, recently burned terrain, which makes predicting the spatial distribution of large debris flows challenging. Studies from rainfall-induced landslides in unburned areas show evidence of hydroclimatic tuning of landslide triggering, such that the spatial distribution of events is best predicted by the observed rainfall anomaly relative to climatic norms rather than by absolute rainfall. In this paper, we test whether the spatial distribution of debris flows in response to rainfall can be similarly predicted by rainfall anomaly. The 520 km2 Dolan Fire burn scar in Monterey County, California, USA, spans a sharp hydroclimatic gradient and experienced a widespread storm in January 2021 that triggered floods and debris flows, providing a natural experiment in which to test this hypothesis. In this study, we use remote and field methods to map debris-flow response and examine its spatial heterogeneity. Together with rainfall data, our mapping reveals that the observed anomalies in peak 15-min rainfall intensity (I15) relative to the intensity of the 1-yr return interval storm predict debris-flow occurrence better than the absolute peak I15. Our findings indicate that debris-flow processes and threshold rainfall required for debris-flow initiation may be tuned to local hydroclimate.

THE IMPACT OF GEOLOGICAL AND RAINFALL CHARACTERISTICS ON SLOPE STABILITY ANALYSIS IN SHALLOW LANDSLIDE MODELLING USING THE TRIGRS MODEL

West Java Province has a high potential for susceptibility to landslides. West Bandung Regency is one of the regions in West Java which has high occurrences of landslides triggered by rainfalls. Rainfall-induced landslides in this region have occurred 165 times during rainfall in the last ten (10) years. An effort to predict the landslide occurrences in this region requires the knowledge of factors affecting the hillslope stability. This paper aims to (1) evaluate the parameters affecting slope stability, (2) determine rainfall characteristics affecting slope, and (3) evaluate the performance of TRIGRS modelling. This study applies a deterministic method using the model of TRIGRS to analyze the effect of one-day antecedent rainfall intensity on the instability of hillslopes. The parameters used in this study are based on field investigation, geological and topographical maps, soil engineering properties, and historical rainfall data. The effect of one-day antecedent rainfall intensity was considered in two scenarios, i.e. 6 hours and 12 hours of antecedent rainfall. Modelling results show that hillslope instability in the study areas is affected by many factors, such as hilly morphology, steep slopes, low shear strength parameters, weathering of volcanic rocks, and the lineament density of geological structures. The modelling simulation also indicates that landslide occurrences in this study area are initiated by a long antecedent rainfall period. Based on ROC analysis, the TRIGRS model shows that the model performance evaluation is in good agreement for modelling slope stability against actual landslide events.