In this study multi-hazard risk assessment is carried out in Arithang ward, one of the major wards within Gangtok Municipal Corporation, with the objectives of (a) landslide and earthquake hazard mapping of Gangtok city with analytical hierarchy process (b) vulnerability mapping in Arithang ward and (c) semiquantitative and semiqualitative risk analysis. Landslide hazard zonation (LHZ) depicts that very high and high hazard zone occupies 6% and 17% of the Gangtok city whereas 60% and 18% of area falls under medium and low hazard category respectively. With respect to seismic hazard susceptibility 13% and 22% of area falls under very high and high category respectively. Semiquantitative risk analysis reveals that majority of the residential buildings are concentrated in low earthquake and landslide hazard zone followed by 39% and 35% within medium class. Only 0.6% and 7% of residential buildings are found in high earthquake and landslide hazard zones. Bamboo and wood made buildings are found to cluster within very high class of landslide hazard. About 61% of multistoried buildings are placed within low zone of LHZ. Risk analysis reveals that buildings at the eastern and western part of Arithang ward come under high risk with respect to earthquake and landslide.

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The Sikkim state, including Gangtok, is dominated by Precambrian rocks which contain foliated schists and phyllites; slopes are therefore susceptible to frequent landslides. The recent development of roads and building structures make this region more vulnerable to landslide hazard. In this research work, landslide susceptibility zonation mapping within Gangtok Municipal Corporation (GMC) area have been carried out implementing remote sensing and GIS technique. To derive the landslide susceptibility map (LSM) of GMC, weighted overlay method (WOM) was implemented by assigning weights to various triggering factors via expert opinion. The twelve triggering factors used in the study were geology/lithology, slope morphometry, lineament density, water regime, rainfall, elevation, soil type, soil liquefaction, soil thickness, building density, relative relief, and land use/land covers (LULC). The final LSM of GMC shows that about 19.14% of the study area falls under very high landslide hazard zone and 31.78% area falls under the high category. Medium and low landslide hazard zone encompasses about 30.95% and 18.11 % of the total area, respectively. The modelgenerated LSM is validated with past reported landslide event where an overall accuracy of above 80% is observed.

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Most parts of the Indian Himalaya fall in seismic zone V and IV, indicating a high degree of susceptibility to earthquakes. Although numerous studies on earthquake risk assessment have been done by different researchers yet very few of these studies and reports have focused on landslides related to earthquakes. It has been observed globally that many casualties during an earthquake in a hilly terrain are attributed to the incidences of landslides triggered by the earthquake and the response actions are also hurdled by the rockfalls/landslides along the highways. Field observations have indicated that such landslides are often associated with earthquakes of magnitude 4 or more. About 20-25 % losses during earthquakes in hilly terrains have been attributed to landslides. The earthquake triggered landslides have affected even the structures and buildings which were well constructed but adversely located in the ground. However, a perusal of seismic zonation studies indicate that landslides have not received due attention. Similarly most of the landslides hazard zonation studies do not consider the impacts of earthquakes in generating numerous and large landslides. Hence, the present paper emphasizes the significance of earthquake related landslides in the hilly terrains through experiences from the past incidences of landslides during earthquakes along with their impact and proposes its consideration in future earthquake risk management programmes as well as in landslides hazard zonation studies for effective risk reduction strategies. The significant earthquakes that affected the Himalayan terrain include Assam (1897), Kangra (1905), Bihar-Nepal (1934), Shillong (1950, Bihar-Nepal (1988), Uttarkashi (1991), Chamoli (1999, Kashmir (2005) etc., that caused heavy damages/losses as well as casualties which were found partly related to the ground and slope failures during these earthquakes. A study of landslides associated with earthquakes has lead to identification of morphological, lithological, tectonic, hydrological and landuse conditions that govern the occurrence of such landslides. For example, most of earthquake triggered landslides/rockfalls happened on convex slopes whereas rain-induced landslides are more common on concave slopes. The concentration of landslides and their size has also been found proportional to the magnitude of the earthquake to some extent. An attempt has also been made to differentiate freshly triggered and reactivated co-seismic landslides in earthquake affected areas as well as post-seismic landslides.

Disaster management requires not only efficient technical, administrative, financial and legal systems within the affected community but also a strong political, socio-cultural and ethical framework. Although it is possible to improve the systems through the implementation of guidelines, plans, procedures, codes and other regulatory measures, it is difficult to change the attitude of the individuals, community, executives and the political leaders that affects the working environment and functional capabilities of these systems. In such situations, ethical practices and moral values play an important role in positively nurturing the environment. The experiences often reveal a lack of good cooperation, coordination and team spirit among different stakeholders, leading to unnecessary delays and inaction or wrong actions for disaster management. The approach is mostly ad-hoc, isolated and uncoordinated. There is hardly any networking, linkage and coordination to work in a multidisciplinary and integrated manner. However, the recent policies and strategies related to disaster risk reduction are now focusing on a systematic way of coordination, linkage and networking for sharing information on resources, knowledge and practices. This paper illustrates the relevant issues with examples from some popular stories/poems that will help the reader to understand the significance of these issues.

Landslides are common and frequent occurring phenomenon in hilly terrain during monsoon season. The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. Landslide inventories are prepared based on LISS-IV (MX) satellite imagery, Google Earth and reported data of Geological Survey of India. Altogether 12 landslide conditioning factors viz. slope, elevation, curvature, aspect, land use/land cover, geology, lineament, rainfall, soil type, soil thickness, water regime and distance from road are considered as input data for geospatial modelling of landslide susceptibility. Finally, model-derived landslide susceptibility maps are classified into four hazard zones, i.e. low, medium, high and very high. To measure model compatibility model comparison is performed in ArcGIS environment and models performance is assessed by confusion matrix where RF-FL gives more accuracy of 69.36% than other two models with 9.68% and 19.35% of Type I and type II error, respectively. The outputs are validated using success and prediction rate method where, RF-PLR, RF-FL and RF-IOE show area under curve (AUC) of success and prediction rate as 76%, 67%, 83%, 78% and 85%, 80%, respectively. Additionally, the differences in model performances were analyzed by means of Wilcoxon signed rank test, where it was found that statistically differences in the performance was significant in case of RF-PLR vs. RF-FL and RF-PLR vs. RF-IOE.

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