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.

Highlights Landslides can be predicted using empirical and probabilistic rainfall thresholds.  Soil moisture is critical in slope stability as it affects the infiltration rate.  Soil moisture can be used with conventional thresholds for better performance.  Idukki (India) is highly a highly susceptible landslide zone in the Western Ghats.  Critical rainfall conditions, considering the soil wetness are derived for Idukki. Highlights (for review)Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning

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–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 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 Système Hydrologique Européen 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.

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 (2017–2019), 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.

Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.

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—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.

No abstract