International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Information Value Model based mapping of updated spatial and temporal landslide susceptibility: a case study from East Sikkim district, India's Northeastern Himalayas. Md Fariduddin Rafique1, Varun Joshi2 1University School of Environment Management, Guru Gobind Singh Indraprastha University, New Delhi, India. 2 University School of Environment Management, Guru Gobind Singh Indraprastha University, New Delhi, India.
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Abstract - The Indian Himalayan Region (IHR), due to its
been caused by recent big earthquakes that have occurred in China (1999), Kashmir (2005), China (2008, 2010, 2013), Sikkim (2011), Nepal (2015), New Zealand (2016), Japan (2018), etc. Numerous people were killed, injured, and infrastructure was damaged, particularly since the road networks were disrupted, as a result of these disastrous occurrences. 1.3% of fatalities of all-natural disasters died through landslides, with Asia accounting for around 54% of these landslides. In recent years, landslides have accelerated in both wealthy and underdeveloped nations due to rapid urbanisation and development [65]. Many fatalities worldwide are caused by natural occurrences like earthquake-induced landslides [27,28,38,45,58]. The majority of the landslides take place in regions with active tectonics, uneven topography, and high rates of precipitation. The geographic distribution and intensity of landslides are influenced by topographic features, lithology, geomorphology, land use, and land cover [3]. The Himalayan mountain region's population and infrastructure are always under risk due to mass migrations. Due to the predominately mountainous topography of the NE Himalaya, landslip activity is seen as a severe issue that threatens both infrastructure and habitation. Thousands of landslides occurred in Indian Himalayan Region (IHR) and its adjoining areas as a result of the catastrophic 2005 & 2011 earthquake in Kashmir and Sikkim [44,59]. Massive landslides, rock avalanches, and other slope collapses that occur often have caused severe casualties and significant infrastructure damage [9,5,44,46]. There have been many studies done in the past to identify the distribution of landslides, field data collecting techniques, inventory development, and geographic distribution analysis [7,6,36,44,57] as well as to understand the mechanics, distribution, and evolution of earthquake-triggered landslides. The territory has been divided into several susceptible zones using the methodologies of landslip susceptibility, including knowledge-based, statistical, deterministic, probabilistic, and machine learning (ML) [5,26,33]. An efficient method for preventing and reducing landslides across a large territory is land-slide susceptibility assessment. It is one of the most helpful informational resources for decision-makers and aids experts in lowering the danger to life and property. In recent years, a number of methods for assessing landslide susceptibility have been created, all of which are based on the idea that future mass movements may be predicted by
topography, geography, and active tectonics, a rough mountain zone, is among the most vulnerable zones to the landslip danger. The most cutting-edge and accurate ways for creating a landslip susceptibility model (LSM) are advanced statistical techniques. The goal of the current work was to use advanced statistical techniques to analyze and evaluate the updated landslip susceptibility for East District in the NE Himalayas of Sikkim, India. The spatiotemporal landslip inventory for the years are produced using literature surveys, historical satellite imageries and on-site observations. Slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI), rainfall, drainage density and land use/ land cover (LULC) are some of the topographic, environmental, geologic, and anthropogenic factors that were included in the spatial database. These LCFs were chosen to study the area's periodic landslip vulnerability. An inventory of 151 landslides from historical published records, field visits and Imagery interpretations, respectively, were used in the experimental design. Information Value Model (IVM), was used to evaluate the vulnerability to landslides as determined by fifteen LCFs. The goal of the study is to help in reducing the number of fatalities and possible economic harm caused by the region's frequent slope instabilities. It is expected that the application of statistical algorithms would assist relevant authorities and organizations in properly planning for and managing the region's landslip threat. Key Words: East Sikkim, Landslide susceptibility, Information Value Model
1.Introduction The most significant geo-environmental risk that is seen in mountainous terrains across the world and poses a serious danger to infrastructure and human life is landslides. Landslides are one of the main risks brought on by natural events like earthquakes and rains, as well as human activities like road construction and urbanisation that may result in slope collapses. Almost 9% of all-natural disasters globally include landslides. Large-scale slope failures have
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