Articles written in Journal of Earth System Science

    • GIS-based pre- and post-earthquake landslide susceptibility zonation with reference to 1999 Chamoli earthquake


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      Landslides induced due to monsoon rainfall and earthquakes are very common phenomena in Uttarakhand Himalayas of India. For example, many such landslides got induced and reactivated by the 1999 Chamoli earthquake. In view of above, authors have made an attempt to prepare pre- and post-earthquake landslide susceptibility zonation (LSZ) maps for a part of Chamoli district, Uttarakhand, India. The novelty of this work lies in producing an LSZ map considering peak ground acceleration (PGA) as one of the controlling factors for earthquake-induced landslide occurrences and validating the LSZ map with the post-earthquake landslide inventory. For this purpose, a spatial database of seven controlling factors, i.e., slope angle, slope aspect, slope curvature, geology, distance to drainage, normalized difference vegetation index (NDVI) and peak ground acceleration (PGA) was prepared in Geographic Information System (GIS). Then, relative frequency ratio (RFR) method was adopted for the LSZ maps. The landslide inventory of 276 landslides (220 pre-earthquake and 56 post-earthquake landslides) was prepared for the study area. Firstly, an LSZ map was generated using six controlling factors excluding PGA and the pre-earthquake landslide inventory (Case I). In another attempt, the LSZ map is prepared using seven controlling factors including PGA and pre-earthquake landslide inventory to examine the influence of seismic parameter (PGA) in landslide susceptibility assessment (Case II). Subsequently, pre- and post-earthquake landslide inventory along with seven controlling factors were used to construct another LSZ map (Case III). Finally, these three LSZ maps were validated and compared with the training and testing data. In this study, a spatial predictive model for earthquake-induced landslide is developed.

    • A data driven efficient framework for the probabilistic slope stability analysis of Pakhi landslide, Garhwal Himalaya


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      The stability of slope is usually characterized by many parameters which are mostly uncertain in nature. Deterministic approach is usually followed to calculate the factor of safety of a slope, but it does not depict the true state of the slope. Hence, a probabilistic approach is a better alternative, which can quantify the probability of failure of a slope under uncertain input parameters. In the present study, slope stability assessment of Pakhi landside is carried out using finite element modelling (FEM) to ascertain the stability conditions of the multilayer slope under both deterministic and probabilistic framework. The multilayer configurations of the profiles are established from the electrical resistivity tomography (ERT). The shear strength reduction (SSR) method is employed to determine the critical strength reduction factor of the slope considering four random variables, namely, cohesion (c), angle of internal friction ($\phi$), Poisson’s ratio (ν) and elastic modulus (E) of each individual layers. The deterministic factor of safety values along two considered profiles namely, section XX$^{\prime}$ and YY$^{\prime}$ are calculated as 1.41 and 1.25, respectively. A data driven machine learning algorithm is used to build a computationally efficient surrogate model to perform Monte Carlo Simulations (MCS). MCS are performed for two different values of coefficient of variation, i.e., 5% and 15% for all the four random variables of all the layers. The proposed method has no idealization regarding the layering configuration and the failure surface. Probabilistic analysis has been made exhaustive and computationally efficient. The probabilistic analysis indicates good adherence with the recent landslide incident in the field. Further, the analysis indicates that the proposed methodology is favourable and useful tool for the system reliability analysis of landslide slopes.


      $\bullet$ Electrical resistivity tomography (ERT) interpretations could delineate sub-surface geometry of lithological layers of the landslide slope.

      $\bullet$ Numerical simulation is performed using the finite element modelling.

      $\bullet$ Sampling of random variables is done using the Latin Hypercube sampling technique.

      $\bullet$ Probabilistic analysis is performed using the MARS-based surrogate model for Monte-Carlo simulation (MCS) with two values of CoV, viz., 5% and 10%.

      $\bullet$ Efficiency of machine learning algorithm to incorporate with the stand alone FE code is demonstrated to build a surrogate model for an efficient probabilistic slope stability analysis.

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