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This study explores the coupling effect of pond ash (PA) and polypropylene (PP) fiber to control the strength and durability of expansive soil. The PA is used to chemically treat the expansive soil and PP fiber is adopted as reinforcement against tensile cracking. The sustainable use of PA and PP fiber are demonstrated by performing mechanical (i.e. unconfined compressive strength, split tensile strength and ultrasonic pulse velocity), chemical (pH value, electrical conductivity and calcite content), and microstructural analyses before and after 2nd, 4th, 6th, 8th and 10th freezing-thawing (F-T) cycles. Three curing methods with 7 d, 14 d and 28 d curing periods are considered to reinforce the 5%, 10%, 15% and 20% PA-stabilized expansive soil with 0.25%, 0.5% and 1% PP fiber. In order to develop predictive models for mechanical and durability parameters, the experimental data are processed utilizing artificial neural network (ANN), in association with the leave-one-out cross-validation (LOOCV) as a resampling method and three different activation functions. The mechanical and durability properties of the PA-stabilized expansive soil subgrades are increased with PP fiber reinforcement. The results of ANN modeling predict the mechanical properties perfectly, and the correlation coefficient (R) approaches up to 0.96.

Microbially induced calcite precipitation (MICP), a sustainable approach for sand biocementation, was investigated in previous studies based on metabolic activity of individual microorganisms. The individual bacteria, specifically Sporosarcina pasteurii (SP), Bacillus subtilis (BS), and Lysinibacillus sphaericus (LS), were found capable enough for sand biocementation. However, present study investigates synergistic effects of using bacterial-hybrids on cementation and consequent improvement in sand properties. The SP, BS, and LS strains were used in different combinations to create bacterial-hybrids and applied under simulated non-sterile field conditions. Initially, sand biotreatment was carried out in plastic tubes up to 14 days, using bacterial mixtures and 0.5 M cementation solution. Biocemented specimens were tested for calcite precipitation, XRD, FTIR, and SEM. The SP and LS combination (SPLS hybrid) showed maximum calcite precipitation, which is further used for biotreatment to create cylindrical sand samples for testing improved engineering properties. These samples were prepared using 0.5 M cementation solution in three pore volumes (1, 0.75, and 0.5 PV) and treatment cycles (12, 24, and 48 hrs TC) up to 18 days. Biocemented samples were tested for permeability (6th, 12th, and 18th days of biotreatment), unconfined compressive strength (UCS), split tensile strength (STS), ultrasonic pulse velocity (UPV), and consolidated undrained stress-strain response. Durability of biocementation was also investigated by determining reduction in strength and UPV subjected to freeze-thaw (FT) cycles (5, 10, 15, and 20). The results showed maximum UCS of 1902 kPa, STS of 356 kPa, UPV of 2408 m/s, and coefficient of permeability reduction up to 91%. The higher results were achieved with 11.11% calcite content in 1PV-12TC treated samples. The 1PV-12TC treated samples resulted in 4.2%, 8.3%, 17%, and 35% reduction of strength after 5, 10, 15, and 20 FT cycles, respectively. Overall, biocementation using hybrid bacteria is shown significant to improve sand's engineering properties, including potential to mitigate liquefaction.

In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspa...

The Indian Himalayan region has been severely affected by landslides causing an immense loss in terms of human lives and economic loss. The landslides are usually induced by rainfall which can be slow and continuous or heavy downpour. The incidences of landslide events in Indian Himalayas have been further aggravated due to the rapid increase in urbanization and thus its increasing impact on socio-economic aspects. There is a dire need for understanding landslide phenomena, estimating its occurrence potential and formulating strategies to minimize the damage caused by them. One of the most affected area is Kalimpong of Darjeeling Himalayas where significant studies have been conducted on zonation, threshold estimation and other related aspects. However, a comprehensive study in terms of temporal prediction for this region remains unattended. The paper deals with assessing landslide hazard using a rainfall threshold model involving daily and cumulative antecedent rainfall values for landslide events. The threshold values were determined using daily rainfall and antecedent rainfall using precipitation and landslide records for 2010–2016. The results show that 20-day antecedent rainfall provides the best fit for landslide occurrences in the region. The rainfall thresholds were further validated using rainfall and landslide data of 2017, which was not considered for threshold estimation. Finally, the results were used to determine the temporal probability for landslide incidence using a Poisson probability model. The validated results suggest that the model has the potential to be used as a preliminary early warning system.

Earthquake-induced liquefaction causes soil to exhibit fluidlike behavior due to a sudden increase in pore water pressure and a concurrent decrease in effective stress. The liquefaction ca...

Microbially induced calcite precipitation (MICP) is a recently developed technique for microbiological ground improvement that has been applied for mitigating various geotechnical challenges. However, the major challenges, such as calcite precipitation uniformity, presence of different bacteria, cementation solution optimization for cost reduction, and implementation under non-sterile and uncontrolled field environment are still not fully explored and require detailed investigation before field application. This study aims to address these challenges of MICP to improve the geotechnical properties of sandy soils. Several series of experiments were conducted using poorly graded Narmada River (India) sand, which were subjected to various biotreatment schemes and tested for unconfined compressive strength (UCS), split tensile strength (STS), ultrasonic pulse velocity (UPV), hydraulic conductivity (after 6 d, 12 d, and 18 d of treatment), and calcite content. The microstructure of sand was examined through a scanning electron microscope (SEM). Initially, the sand was individually augmented with two non-pathogenic bacterial strains, i.e. Sporosarcina (S.) pasteurii and Bacillus (B.) sphaericus. The stopped-flow injection method was adopted to provide cementation solutions at three different durations (treatment cycle) of 12 h, 24 h, and 48 h and three different pore volumes (PVs) of 1, 0.75, and 0.5. The pore volume here refers to the porosity which is expressed as a ratio, i.e. a porosity of 50% was used as 0.5. The results showed rock-like behaviors of biocemented sand with the UCS, STS, and UPV enhancement up to 2333 kPa, 437 kPa, and 2670 m/s, respectively. The hydraulic conductivity reduction of 96.6% was achieved by 12% of calcite formation after 18 d of treatment using Sporosarcina pasteurii, 12-h treatment cycle, and one pore volume of cementation media in each cycle. Overall, a 24-h treatment cycle and 0.5-pore volume cementation solution were found to be the optimal treatment which was effective and economical to achieve heavily cemented, rock-type biocemented sand using both bacteria.

Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area.

Metabolites generated from microorganisms have a high potential for the strength improvement and contaminant remediation of soils. The consortium of naturally available bacteria and microa...

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity&ndash, Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective.

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta&mdash, integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region, (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used, the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Syst&egrave, me Hydrologique Europ&eacute, en Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.

The Indian Himalayan regions have been significantly affected by the increase in the frequency of landslide occurrence. Thirty percentage of the worldwide landslide incidents occur in the Himalayan region with 42% of India’s landslide region belonging to Darjeeling–Sikkim Himalayas (NDMA Report, 2011). Several studies have been carried out worldwide on early warning systems considering rainfall history. Although rainfall is one of the criteria which can successfully predict the probability of landslides on a regional scale, it is indeed difficult to understand the risk associated with slope failure. The reason is the spatial variation of rainfall intensity, local soil conditions, geology, hydrology and topography. An early warning and monitoring system is one of the most effective techniques to minimise the disasters influenced by slope instability as it is less expensive and easier than slope reinforcement. A reliable and robust system fitted with microelectromechanical systems tilt sensor and volumetric water content sensors were installed. The sensor monitors the tilting angle of the instrument which was installed at shallow depths, and the variation of tilting angle corresponds to lateral displacement at slope surface. The primary objective was to monitor the tilting angles at various slope sites and to formulate a dependable warning system with a low probability of false alarms in Chibo Pashyor region in the Indian state of West Bengal. Such system would assist in the installation of several low-cost sensors over an active slope as it was difficult to determine the particular section of slope which could fail in the next heavy rainfall. The change in tilting rate would help in establishing thresholds determined empirically. Such system would help in developing an early warning system and also help in calibrating thresholds calculated using empirical techniques.

Landslides triggered by heavy rains are increasing in number and creating severe losses in hilly regions across the world. Rainfall thresholds on regional and local-scales are being used for forecasting such events, for efficient early warning. Empirical and probabilistic approaches for defining rainfall thresholds are traditional tools which are being used as part of the forecasting system for rainfall induced landslides. Such methods are easy-to-use and are based on statistical analyses. They can be derived without looking into the complex hydro-geological processes involved in slope failures, but are often associated with the disadvantage of higher false alarms, limiting their applications in a regional landslide early warning system (LEWS). This study is an attempt to improve the performance of conventional meteorological thresholds by considering the effect of soil moisture, using a probabilistic approach. Idukki district in southern part of India is highly susceptible to landslides and has witnessed major socio-economical setbacks in the recent disasters happened in 2018 and 2019. This tourist hub is now in need of a landslide forecasting system, which can help in landslide risk reduction. This study attempts to understand the effect of averaged soil moisture estimates derived from passive microwave remote sensing data, for improving the performance of conventional empirical and probabilistic thresholds. For defining empirical thresholds, an algorithm-based approach such as Calculation of Thresholds for Rainfall-induced Landslides Tool (CTRL-T) has been used. Probabilistic thresholds were defined using a Bayesian approach, finding the posterior probability of occurrence using the marginal and conditional probabilities of the control parameters along with the prior probability of occurrence of landslide. The derived rainfall thresholds were quantitatively compared with the Bayesian probabilistic threshold derived using rainfall severity and soil wetness using an area under the curve (AUC) based on receiver operating characteristics (ROC) curve method. The results show that when the antecedent moisture content in soil is less, only severe rainfall events can trigger landslides in the study area; while less severe rainfall events can also trigger landslides when the soil is wet. The role of soil wetness in the initiation is used to improve the performance of the conventional methods, and a ROC approach was used for the statistical comparison of different models. Further, the results indicated that the probabilistic threshold using rainfall severity and soil wetness outperformed the conventional approaches with AUC of 0.96, being the most sensitive and specific among the models considered. This result opens new promising perspectives for the development of an operational LEWS in the Idukki district based on a combination of rainfall and soil moisture data. Moreover, this work contributes to strengthen the advancing trend of hydro-meteorological thresholds based on soil moisture, which is gaining a growing attention in landslide studies and that, to date, was lacking evidences in monsoon regions.

The recently developed microbially induced calcite precipitation (MICP) method is a potentially effective and environmentally friendly technique to improve the unfavorable soil conditions. However, the durability of biotreated sand under freeze-thaw cycles has not been investigated. This study investigates the effects of freeze-thaw (FT) cycles on shear strength and shear modulus of Narmada river sand (a poorly graded liquefiable sand) treated with MICP under different conditions. The bacterial growth and biotreatment were conducted under non-sterile and environmentally uncontrolled conditions that are typically encountered in the field conditions. First, the effects of main control variables involved in the MICP treatment, specifically two bacteria strains i.e., Sporosarcina pasteurii (S. pasteurii) and Bacillus sphaericus (B. sphaericus), two treatment cycles viz. 12 h and 24 h, and different pore volumes (1 PV, 0.75 PV, and 0.5 PV) of cementation solution injection, were investigated. After 18 days of continuous treatment, the biocemented specimens were tested for unconfined compressive strength (UCS), split tensile strength (STS), hydraulic conductivity, ultrasonic pulse velocity (UPV), and shear modulus after 0, 5, 10, 15 and 20 freeze-thaw (FT) cycles. The calcite content distribution in all of the biotreated samples was also analyzed. The results demonstrated that the maximum UCS, STS, UPV, shear modulus, and reduction in hydraulic conductivity were 2333.2 kPa, 437.7 kPa, 2670 m/s, 13 GPa, and 96.6%, respectively, at 12% calcite content using S. pasteurii, 12 h treatment cycle, and injection of 1 PV cementation solution in every cycle up to 18 days. The biocemented specimens maintained almost 90% shear strength and 77% shear modulus after 10 FT cycles in 9–12% calcite precipitated samples augmented with S. pasteurii. The similar biocemented samples resulted in 14–17% and 31–35% decrease in shear strength, and 41–44% and 54–57% decrease in shear modulus after 15 and 20 FT cycles, respectively.

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017&ndash, 2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

The Indian Himalayan locale has been essentially influenced by the increase in the frequency of landslide events. Out of 0.42 million km2 of India’s landmass prone to landslides, 42% falls in the North East Himalaya, especially Darjeeling and Sikkim. The harm due to landslides is massive, causing loss of life, property and agricultural land, thus initiating a dire need for formulating strategies to minimize its impact. There have been many attempts to establish rainfall thresholds on global, regional and local scales which compare analysis at various levels. Rainfall thresholds anticipate landslide occurrence and help in issuing a warning to civil authorities and the general population. However, empirical relations defining the relationship between landslide occurrences and rainfall events in Kalimpong remain unattended. In this paper, rainfall thresholds for landslide occurrence have been ascertained for Kalimpong area of Darjeeling Himalayas, in the Indian province of West Bengal. A threshold for landslide occurrences which describes intensity–duration threshold was estimated using the power law equation. The relationship for the study area is I = 3.52 D−0.41 (I is rainfall intensity (mm/h) and D is duration (h)). Results show that events with a rainfall intensity of 0.95 mm/h with a duration of 24 h have a high risk of slide initiation in this region. It also demonstrates that for 10- and 20-day antecedent rainfall, an intensity of 88.37 and 133.5 mm is required for landslide occurrence in this region. Such data would help in implementing early warning systems that focus on rainfall thresholds and forecasting rainfall measurements. Rainfall thresholds for landslide initiation in Kalimpong can be enhanced with more precipitation and landslide data as and when available.

Liquefaction is a complex phenomenon where the soil is subjected to high strain levels due to dynamic loading at the time of seismic events, contributing to severe damage and collapse of the structures. The effect of liquefaction can be predicted in three stages, i.e., evaluation of susceptibility, hazard and deformations. Soil susceptibility to liquefaction can be assessed using the geotechnical properties such as grain size, age of the soil deposit, penetration resistance of the soil, whereas hazard can be assessed in terms of factor of safety estimated against liquefaction. In the present study, one-dimensional equivalent linear ground response analysis has been carried in the study area Vishakhapatnam, Andhra Pradesh (India) and the surface peak ground acceleration (PGA) has been estimated using DEEPSOIL. The surface PGA values obtained have been used in liquefaction hazard assessment using stress-based method (SBM) and energy-based method (EBM). Hazard maps were generated from the estimated values of PGA and factor of safety against liquefaction (F L). The surface PGA values obtained are in the range of 0.09–0.14 g which are higher than 0.1 g (limiting value provided by Seismic code of India (IS: 1893-2002)). It is observed that the locations in the central part of the city are prone to higher accelerations comparatively. From liquefaction potential assessment, it is evident that most of the locations in northern and central parts of the city are prone to liquefaction. As the study area has two operational ports and is highly industrialized, findings from the present study will be helpful in retrofitting, analysis and design of structures, therefore, solving practical challenges in structural and geotechnical engineering.

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D−0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall&ndash, rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010&ndash, 2016. The results signify that a rainfall event of 48 hours with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system.

The Himalayan region with its complex geological features, steep slopes, rugged topography, and intense monsoonal rainfall create ideal conditions for landslides. The damage caused due to landslides is immense causing significant loss of life and property initiating a dire need to formulate strategies in minimizing its impact in areas affected by landslides. Several attempts have been presented by various researchers in order to establish rainfall intensity thresholds using various parameters of rainfall conditions. These methods provide deterministic thresholds, i.e., landslide or no landslide; such thresholds are not always suited for landslides. In this paper, rainfall thresholds have been evaluated using a statistical method which results in the probability of landslide occurrence for single or multiple rainfall parameters leading to slide initiation. The results are expressed in terms of probabilities by analyzing two different variants of Bayes theorem, i.e., 1D and 2D. Probabilistic thresholds were calculated for Kalimpong region of Darjeeling Himalayas using available rainfall and landslide data during the year 2010–2016. The probabilities calculated for landslide occurrence were found to be 0.37 for rainfall intensity greater than 10 mm/day. However, the probability for a combination of rainfall intensity of 30 mm/day with duration of 3 days was calculated to be 0.67. The results also depicted that landslides are related to rainfall event parameters especially with rainfall intensity.