Abstract. Mumbai city is the financial capital of India and is fifth most densely populated city in the world. Seismic soil liquefaction is evaluated for Mumbai city in terms of the factors of safety against liquefaction (FS) along the depths of soil profiles for different earthquakes with 2 % probability of exceedance in 50 yr using standard penetration test (SPT)-based simplified empirical procedure. This liquefaction potential is evaluated at 142 representative sites in the city using the borehole records from standard penetration tests. Liquefaction potential index (LPI) is evaluated at each borehole location from the obtained factors of safety (FS) to predict the potential of liquefaction to cause damage at the surface level at the site of interest. Spatial distribution of soil liquefaction potential is presented in the form of contour maps of LPI values. As the majority of the sites in the city are of reclaimed land, the vulnerability of liquefaction is observed to be very high at many places.

The seismicity of the National Capital Region (NCR) of India increased significantly over the last decade. Communities in the NCR face significant exposure to damaging seismic events, and the seismic risk arises not only from the region’s proximity to the Himalayan mountains, but also from the socioeconomic vulnerabilities in its communities and the current capacities of different localities to respond to and recover from any unforeseen large seismic event. GIS-based spatial distribution of exposure to seismic hazards (SH) can help decision-makers and authorities identify locations with populations at high seismic risk, and to prepare risk-mitigation plans. Socioeconomic vulnerability (SeV) studies serve as a basis for quantifying qualitative measures. For this purpose, in the present study, the hazard of place (HoP) model is used to assess SeV to seismic hazards in the NCR. Social indicators like age, gender, literacy, family size, built environment, etc., comprising a total of 36 variables, are used to assess a socioeconomic vulnerability index (SeVI) based on factor and principal component (PCA) analyses. Based on PCA, 20 variables were retained and grouped into four factors: socioeconomic status, employment status, building typology, and family size. Ground-motion parameters, estimated from probabilistic seismic hazard assessment, are integrated with the socioeconomic vulnerability index to quantify exposure to seismic hazards. The spatial distributions in the produced socioeconomic-vulnerability index and seismic–hazard–exposure maps highlight the critical areas. The results reveal that areas of low literacy, high unemployment, and poor housing condition show moderate-to-high vulnerability. The south-eastern region of the study area is assessed as a high-risk zone by an integrated SeV–SH risk matrix. The results of this study emphasize the importance of the socioeconomic vulnerability component of disaster risk–reduction programs, from a holistic perspective, for the areas with high seismicity.

The state of Meghalaya of the North Eastern Region (NER) of India, situated in the India Himalayan Region (IHR), is the rainiest place in the country and falls under seismic zone V.The Himalayan ranges account for 80% of total landslide hazards in India. The main goal of the present study is to generate the GIS-based landslide susceptibility map (LSM) of Meghalaya by using frequency ratio (FR), Shannon entropy (SE), analytical hierarchy process (AHP), and fuzzy-AHP (FAHP) models and compare these models for the study area. Fifteen landslide conditioning factors are used for susceptibility mapping includes a slope, aspect, elevation, plan curvature, stream power index (SPI), topographic wetness index (TWI), land use land cover (LULC), normalized difference vegetation index (NDVI), distance from the river, road and faults, rainfall (30 years mean annual rainfall), soil texture, geomorphology, and lithology. Landslide inventory of 1330 landslide events is prepared and mapped from various sources. The inventory dataset is randomly split in a 70/30 ratio to make the training dataset (70%) used in the model and testing dataset (remaining 30%) for validation purposes.The southern escarpment, the southeast region of the study area, and hillslope along the roadside show high susceptibility for landslide occurrence in all four models. The LSMs produced in the present study are validated using the area under curve (AUC) value. The 2 presented LSMs can help concerned authorities and planners to make sustainable development plans and formulate risk mitigation strategies keeping in mind the critical areas for landslide hazards.

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