Rainfall-triggered Landslides Research Articles

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.

How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering?

In many areas of the world, the prediction of rainfall-induced landslides is usually carried out by means of empirical rainfall thresholds. Their definition is complicated by several issues, among which are the evaluation and quantification of diverse uncertainties resulting from data and methods. Threshold effectiveness and reliability strongly depend on the quality and quantity of rainfall measurements and landslide information used as input. In this work, the influence of the temporal resolution of rainfall measurements on the calculation of landslide-triggering rainfall thresholds is evaluated and discussed. For the purpose, hourly rainfall measurements collected by 172 rain gauges and geographical and temporal information on the occurrence of 561 rainfall-induced landslides in Liguria region (northern Italy) in the period 2004–2014 are used. To assess the impact of different temporal resolutions on the thresholds, rainfall measurements are clustered in increasing bins of 1, 3, 6, 12 and 24 h. A comprehensive tool is applied to each dataset to automatically reconstruct the rainfall conditions responsible for the failures and to calculate frequentist cumulated event rainfall–rainfall duration (ED) thresholds. Then, using a quantitative procedure, the calculated ED thresholds are validated. The main finding of the work is that the use of rainfall measurements with different temporal resolutions results in considerable variations of the shape and the validity range of the thresholds. Decreasing the rainfall temporal resolution, thresholds with smaller intercepts, higher slopes, shorter ranges of validity and higher uncertainties are obtained. On the other hand, it seems that the rainfall temporal resolution does not influence the validation procedure and the threshold performance indicators. Overall, the use of rainfall data with coarse temporal resolution causes a systematic underestimation of thresholds at short durations, resulting in relevant drawbacks (e.g. false alarms) if the thresholds are implemented in operational systems for landslide prediction.

Structural and Green Infrastructure Mitigation Alternatives Prevent Ciliwung River from Water-related Landslide

Landslide is one of the most dangerous hazards worldwide. It could be caused by several factors and could have a massive destructive impact on the environment. A landslide event occurred in one of the urban cities in Indonesia. In the middle of 2002, a landslide disaster due to rainfall occurred in the Ciliwung River’s floodplain, precisely in the South Jakarta area. The landslide profoundly affected large areas of the region and seriously injured many people. Several circumstances that could trigger landslide occurrences are the building load in the settlement area around the river, increase in the rainfall intensity, slope, and soil characteristics in the Ciliwung River area. This research proposes a combination of nonstructural and structural disaster mitigation methods for water-related landslide by investigating the safety factor (SF) of the river bank’s slope in one of the impacted sites, i.e., the area under the main bridge of Grand Depok City regency. This site is located in the boundary area between Depok City and South Jakarta. The authors simulate analytical and numerical modeling to estimate the SF of the slopes. This research concludes that the minimum SF in the analyzed location is recognized as a safety criterion for society. The condition becomes less secure when an earthquake occurs. Furthermore, high rainfall intensity could become the worst scenario that generates considerable damage. The proposed structural mitigation for river bank with anchor or snail increases the SF. However, this reinforcement program is not recommended because of its high cost and ineffectiveness in solving problems. Hence, green infrastructure (GI) is highly suggested for nature-based mitigation to prevent rainfall-triggered landslides in the Ciliwung River area. The authors conduct the preliminary design of the study and recommend further analysis of GI or soil bioengineering to ensure its effectiveness and applicability in the research area.

Early warning system for rainfall-triggered landslides based on real-time probabilistic hazard assessment

A methodology to define rainfall-landslide thresholds, using a probabilistic model in which the accumulated rainfall at any time is treated as a random variable, is proposed. The region under study is divided into areas of homogeneous rain hazard. For each homogeneous area, a probability model is fitted using state-of-the-art statistical methods, for each accumulation time considered. Thresholds are obtained by the definition of confidence intervals. Instantaneous accumulated rains, measured in real-time, are used to calculate the instantaneous probabilities of a landslide at each area and accumulation time. The maximum instantaneous probability determines the critical accumulated rain and sets the issued warning level. In addition, the model is tested, retrospectively, with the data for the disaster of April 19, 2017, in Manizales, Colombia, where 38 rainfall-triggered landslides killed 17 people and affected more than 3126 families.

Geotechnical characteristics of residual soils in rainfall-triggered landslides in Rize, Turkey

Every year, hundreds of landslides are triggered by rainfall in the city of Rize, northern Turkey, resulting in casualties and devastating social and economic consequences. Although these landslides have been observed every year, for a long time in Rize, there is limited data on characteristics of these soils in the literature. Characterizing these soils is of paramount importance for numerical modeling of the landslide mechanisms, for stabilization works, for landslide susceptibility mapping and for establishing rainfall intensity-duration thresholds. This study seeks to investigate the properties of residual soils decomposed from volcanic rocks that are involved in rainfall-triggered, shallow (< 5m thick) and rapid landslides of the flow type. Disturbed and undisturbed soil samples are taken from a total of 12 landslide sites to evaluate index properties, physical and mechanical properties and the mineralogy. Also, in-situ unit weights are determined on undisturbed samples, and portable hand vane tests are conducted to evaluate the in-situ undrained shear strength. Investigations reveal that soils at the shear surfaces, at the main and side scarps, and within the landslide mass are similar materials, i.e. there is no material difference above and below the shear surface. Results indicate that these materials are relatively loose, medium-stiff, mostly fine-grained soils (low and high plasticity silts and high plasticity organic soils), with liquid limits in the range of 35–87%, with some organic content (1.8–12.1% by dry mass) and have relatively low pH (3.5–5.3). As for the mechanical properties, a total of 72 direct shear tests are conducted on undisturbed samples (which sometimes included a plant root or a stone) in the normal stress range of 10–65kPa, in unsaturated and in saturated condition. The average internal friction angle is 36.3° (range 31.1–38.0°) in the saturated condition. The undrained shear strengths are 10 to 52kPa and 42 to 103kPa from unconfined compression tests and hand vane tests, respectively. The results of this study can be used for future research on rainfall triggered landslides.

Geoelectrical characterization and monitoring of slopes on a rainfall-triggered landslide simulator

In this paper, we present the results of time-lapse electrical resistivity tomography (ERT) monitoring of rainfall-triggered shallow landslides reproduced on a laboratory-scale physical model. The main objective of our experiments was to monitor rainwater infiltration through landslide body in order to improve our understanding of the precursors of failure. Time-domain reflectometry (TDR) data were also acquired to obtain the volumetric water content. Knowing the porosity, water saturation was calculated from the volumetric water content and we could calibrate Archie's equation to calculate water saturation maps from inverted resistivity values. Time-lapse ERT images proved to be effective in monitoring the hydrogeological conditions of the slope as well as in detecting the development of fracture zones before collapse. We performed eight laboratory tests and the results show that the landslide body becomes unstable at zones where the water saturation exceeds 45%. It was also observed that instability could occur at the boundaries between areas with different water saturations. Our study shows that time-lapse ERT technique can be employed to monitor the hydrogeological conditions of landslide bodies and the monitoring strategy could be extended to field-scale applications in areas prone to the development of shallow landslides.

Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India

Sikkim, one of the Northeastern states of India, is a famous tourism spot in the Himalayas with dynamic population density. This mountainous area receives heavy rainfall and is well known for frequent shallow landslides, especially, Chandmari, which is a village, situated in Gangtok area in East Sikkim. Even though it is well known that rainfall and landslides are correlated, Sikkim lacks a well-established landslide early warning system. Such a system is important in this region because it is one of the highest landslide-prone areas in India. The current research attempts to establish rainfall thresholds as part of developing an efficient landslide early warning system for this region. The rainfall thresholds for landslides are derived based on the daily rainfall data available from India Meteorological Department (IMD) for six stations in Sikkim. Analysis of daily rainfall data and landslide events in this area between the year 1990 and 2017 is performed. An intensity–duration (I–D)-based regional rainfall threshold is derived as I = 43.26 D−0.78 (I = rainfall intensity in mm/day and D = duration in days) for the rainfall-triggered landslides in Sikkim region and a local threshold of I = 100 D−.92 was developed for the Gangtok area. Furthermore, the influence of antecedent rainfall in landslide initiation is explored by considering the daily, 3-day, 5-day, 7-day, and 20-day cumulative rainfall values associated with landslides. The proposed threshold equations and study of the effect of antecedent rainfall on landslides are intended to aid in enhancing the real-time landslide early warning system (R-LEWS) being developed for Sikkim.

Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways

The 2015 Gorkha earthquake (Mw = 7.8) caused significant earthquake triggered landslides (ETL) in a landscape that is heavily intervened by rainfall triggered landslides (RTL). China’s Belt and Road Initiative plan to boost South-Asian regional trade and mobility through two key highway corridors, i.e. 1) Longmu–Rasuwa–Kathmandu (LRK) and 2) Nyalam–Tatopani–Kathmandu (NTK) route, that dissect the Himalayas through this geologically unstable region. To understand the spatial characteristics and susceptibility of these ETL and RTL, we delineate the landslides by means of time variant satellite imageries, assess their spatial distribution and model their susceptibilities along the highway slopes. We use a coupled frequency ratio (FR) – analytical hierarchy process (AHP) model by considering nine landslide determinants, e.g. geomorphic type (slope, aspect, curvature, elevation), hydrologic type (erosive potential of gullies, i.e. stream power index and distance to streams), normalized difference vegetation index, lithology and civil structure type (i.e. distance to roads). The results demonstrate that elevation and slope predominantly control both these landslide occurrences. The model predicts locations of ETL with higher accuracy than RTL. On comparison, NTK was safer with 133.5 km2 of high RTL or ETL (or both) landslide susceptible areas, whereas LRK has 216.04 km2. For mapping the extent of these landslides, we constricted it to the slope units of highways to reduce the computational effort, but this technique successfully achieved an acceptable threefold average model prediction rate of 82.75% in ETL and 77.9% in RTL. These landslide susceptibility maps and route comparisons would provide guidance towards further planning, monitoring, and implementing landslide risk mitigation measures for the governments.

Global detection of rainfall-triggered landslide clusters

Abstract. An increasing awareness of the cost of landslides on the global economy and of the associated loss of human life has led to the development of various global landslide databases. However, these databases typically report landslide events instead of individual landslides, i.e., a group of landslides with a common trigger and reported by media, citizens and/or government officials as a single unit. The latter results in significant cataloging and reporting biases. To counteract these biases, this study aims to identify clusters of landslide events that were triggered by the same rainfall event. An algorithm is developed that finds a series of landslide events that (a) is continuous with no more than 2 d between individual events and where (b) precipitation at the location of an individual event correlates with precipitation of at least one other event. The developed algorithm is applied to the Global Landslide Catalog (GLC) maintained by NASA. The results show that more than 40 % of all landslide events are connected to at least one other event and that 14 % of all studied landslide events are actually part of a landslide cluster consisting of at least 10 events and up to 108 events in 1 d. Duration of the detected clusters also varies greatly from 1 to 24 d. Our study intends to enhance our understanding of landslide clustering and thus will assist in the development of improved, internationally streamlined mitigation strategies for rainfall-related landslide clusters.

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation.

The 2017 Regent Landslide, Freetown Peninsula, Sierra Leone

At 06:50 on Monday 14 August 2017, a hillslope on the Freetown Peninsula, Sierra Leone, collapsed, sending 300 000 m 3 of debris into the flooded valley below. As this debris mixed with floodwater it became a sediment-laden flood which entered a drainage channel and travelled 6 km to the coastline. The event destroyed nearly 400 buildings, claimed the lives of an estimated 1100 people and affected c. 5000 people. The mechanism was a two-stage rainfall-triggered landslide followed by a channelized debris-laden flood. The processes were similar to the nearby 1945 event in Charlotte, which killed at least 13 people. Geomorphological mapping has identified evidence of hundreds of other large landslides that occurred before modern records, providing an appreciation of the slope processes affecting the Freetown Peninsula. Following the 2017 Regent Landslide, rehabilitation of the affected area involved a risk-reduction strategy that centred on reducing population exposure. These events are a reminder that the steep slopes and valleys across the Freetown Peninsula are highly susceptible to rainfall-triggered landslides which, given the topography, have a high propensity to generate high intensity landslides and debris-laden floods. Future urbanization must consider whole-catchment management, flooding and slope engineering issues to provide lasting landslide risk reduction.

Engineering geomorphological investigation of the Kasavu landslide, Viti Levu, Fiji

Rainfall-triggered landslides are prevalent along Fiji’s main roads, but detailed studies of factors pre-conditioning slopes to fail and slope failure mechanisms are sparse. This study examines the factors leading to the Kasavu landslide, a small, shallow rotational landslide that transitioned into an earthflow along the Kings Road in Viti Levu, Fiji. The Kasavu landslide was triggered on 17–18 December 2016 by prolonged rainfall from a tropical depression. The extensive damage from the landslide resulted in closure of a section of the Kings Road for over a year incurring significant financial costs to the Fijian government and public. Geotechnical tests revealed soils sampled from the Kasavu landslide headscarp to be very stiff, sensitive, and close to the liquid limit. Although having a low clay content, the soils are highly active due to their smectite content. Geomorphic investigation revealed the landslide is located on a > 21° slope near a ridgeline affected by piping and soil erosion processes, above a major floodplain. The primary trigger for the Kasavu landslide was daily rainfall of 176 mm followed by 3-day antecedent rainfall of 361 mm. Pre-conditioning factors included the steep slope, expansive clays, and piping erosion. Dynamic loading from heavy goods vehicles (HGVs) likely also have played a role in slope failure. Considering the current geomorphic setting and soil properties, further landslides in the area are possible. Thus, future management of this section of the Kings Road should consider drainage of the ridgeline and groundwater monitoring, together with provision of load support to existing slopes.

Physical Tank Experiment Investigation on Rainfall Producing Groundwater Level in Homogeneous Material Slopes

It has been recognized that pore water pressure (PWP) changes in response to precipitation play a critical role in rainfall-triggered landslides. Tank models as a kind of undetermined model are widely applied for estimating groundwater levels in slopes. Most of these applications treat the tank models as a theoretical model. Therefore, in this study, physical tank experiments are reported, indicating an evaluation of three typical conceptual tank models (i.e., simple tank model, surface runoff tank model, and lateral water flow supply tank model). To reduce the slope structure controlling affection, the study takes homogenous soil material as the simulation of the slope mass. The experimental results demonstrated how the groundwater tables producing pore water pressure were affected by infiltration time lags, surface runoff, and lateral flow.

Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides

In some rainfall-triggered landslides, intensity-duration thresholds can have limited prediction ability; therefore, investigation of alternative approaches that can be used for temporal prediction of rainfall-induced landslides is needed. This paper presents a methodology for predicting rainfall-induced shallow landslides based on a lumped conceptual hydrological model. The production storage level during the rainfall event and the rainfall sum during the event are used for landslide prediction. Based on these two hydro-meteorological variables a threshold is defined that could be used for rainfall-induced landslides prediction as part of an early warning system. The presented methodology is tested using the meso-scale Selška Sora River catchment case study in western Slovenia where 20 active landslides from the Slovenian National Landslide Database are used to calibrate and evaluate the methodology performance. The results are compared to three different (i.e. local, regional, and global) intensity-duration thresholds. The results of the presented approach are superior in terms of several goodness-of-fit criteria compared to tested local and global ID thresholds. Because only daily rainfall, evapotranspiration, and discharge data are needed to calibrate the selected hydrological model and only daily rainfall and evapotranspiration to run the model, the presented approach could also be useful for data-scarce areas where detailed physically based landslide prediction models that require many data cannot be constructed. Moreover, we have also derived the probabilistic version of the proposed threshold for triggering of shallow landslides using copula functions.

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

Assessment of Rainfall Thresholds for Rain-Induced Landslide Activity in North Sikkim Road Corridor in Sikkim Himalaya, India

Land sliding is a perennial problem in the Eastern Himalayas. Out of 0.42 million km2 of Indian landmass prone to landslide, 42% fall in the North East Himalaya, specially Darjeeling and Sikkim Himalaya. Most of these landslides are triggered by excessive monsoon rainfall between June and October in almost every year. Various attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity – duration of antecedent rainfall models on global, regional and local scale for triggering of the landslide. This paper describes local aspect of rainfall threshold for landslides based on daily rainfall data in and around north Sikkim road corridor region. Among 210 Landslides occurring from 2010 to 2016 were studied to analyze rainfall thresholds. Out of the 210 landslides, however, only 155 Landslides associated with rainfall data which were analyzed to yield a threshold relationship between rainfall intensity-duration and landslide initiation. The threshold relationship determined fits to lower boundary of the Landslide triggering rainfall events is I = 4.045 D - 0.25 (I=rainfall intensity (mm/h) and D=duration in (h)), revealed that for rainfall event of short time (24 h) duration with a rainfall intensity of 1.82 mm/h, the risk of landslides on this road corridor of the terrain is expected to be high. It is also observed that an intensity of 58 mm and 139 mm for 10-day and 20-day antecedent rainfall are required for the initiation of landslides in the study area. This threshold would help in improvement on traffic guidance and provide safety to the travelling tourists in this road corridor during the monsoon.

Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Oshima Volcanic Island, Japan at a regional scale. At first, a landslide inventory map is prepared consisting of 44 landslide polygons (10,444 pixels) from aerial photo-interpretation and field surveys. To estimate the robustness of the models, we randomly adapted two different samples (S1 and S2), comprising of both positive and negative cells (70% of total landslides - 7293 pixels) for training and remaining (30%–3151 pixels) for validation. Twelve causative factors including altitude, slope angle, slope aspect, plan curvature, total curvature, compound topographic index, stream power index, distance to drainage network, drainage density, distance to geological boundaries, lithology and cumulative rainfall were selected as predictors to implement the landslide susceptibility model. The area under the receiver operating characteristics (ROC) curves (AUC) and other statistical signifiers were used to verify the model accuracies. The result shows that the DT and RF models achieved remarkable predictive performance (AUC > 0.9), producing near accurate susceptibility maps. The overall efficiency of RF (AUC = 0.956) is found significantly higher than the DT (AUC = 0.928) results. Additionally, we noticed that the performance of RF for modeling landslide susceptibility is very robust even though the training and validation samples are altered. Considering the performances, we suggest that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making. Moreover, the RF-based model is promising and enough to be recommended as a method to map regional landslide susceptibility.