Estimating rainfall threshold and temporal probability for landslide occurrences in Darjeeling Himalayas

Dikshit, Abhirup; Satyam, Neelima; Pradhan, Biswajeet; Kushal, Sai

doi:10.1007/s12303-020-0001-3

Cited by 32 publications

(8 citation statements)

References 39 publications

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“…Downward movement of the slope forming materials under the in uence of the gravitational forces often take place at the cut slopes along the highway stretches causes loss of life and property every year in hilly areas at global scale (Aleotti and Chowdhury 1999; Dikshit et al 2020). The stabilized original and modi ed slopes are required for the constructional activities of the new transport corridors (Sharma et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Slope stability assessment along Indian Himalayan National Highway NH-154A

Singh

2022

Preprint

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The road cut rock slope stability assessment along the Chamba-Bharmour road corridor of NH-154A has been carried out in present study based on the geological and geotechnical investigations as published In Journal Geological Society of India (Singh and Kumar 2020). The type of failure in the rock slopes has been identified using kinematic analysis based on Markland’s test. The factor of safety (FOS) of identified planar and a wedge failures was evaluated using method proposed by Hoek and Bray. Extensive field work has been performed for collecting rock samples and to record input parameters like joint orientations for stereonet plotting. A total of six out of fourteen potentially unstable rock slopes at different locations along the NH-154A have been taken into consideration for their stability analysis. The unstable rock slope sections L3R1, L16R8, L19R14, L27R12, and L29R13 fulfill the criteria for plane failure conditions while L16R8, L25R10, L26R11, L27R12, L29R13 fulfill the criteria for wedge failure. It has been found that the FOS was identified to be less than one for planar failure at two sites while the minimum value of FOS for planar failure at other two sites falls between 1.0–1.2. The FOS of the plane failure at a single site was evaluated as greater than 2. The wedge failures identified at five sites had FOS greater than one. The site specific mitigation and control measures have been evolved for critical rock cut slopes.

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Section: Introductionmentioning

confidence: 99%

Slope stability assessment along Indian Himalayan National Highway NH-154A

Singh

2022

Preprint

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“…In a recent work, Dikshit et al (2020b) argued the necessity of using satellite-based products in the Indian Himalayan region. A few authors used satellite-based data to explore the correlation between rain and the occurrence of landslides.…”

Section: Introductionmentioning

confidence: 99%

Satellite rainfall products outperform ground observations for landslide prediction in India

Brunetti

Melillo

Gariano

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

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“…Some of the influencing factors of landslides include the topography, geology, hydrology, and land use pattern of the area [3]. In the recent decade, landslide events have increased in both magnitude and frequency due to climate change effect reflected in rainfall patterns [4,5]. erefore, it is desirable to identify landslide-susceptible zones for better landslide management and disaster reduction [6].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.

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Estimating rainfall threshold and temporal probability for landslide occurrences in Darjeeling Himalayas

Cited by 32 publications

References 39 publications

Slope stability assessment along Indian Himalayan National Highway NH-154A

Slope stability assessment along Indian Himalayan National Highway NH-154A

Satellite rainfall products outperform ground observations for landslide prediction in India

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

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