Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ)

Abstract

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.

Landslide Susceptibility Region Mapping Using GIS, Analytic Hierarchy Process Model, and Multi-Criteria Analysis at the Chemoga Watershed, Upper Blue Nile, Ethiopia

“…The weighting overlays are an easy, straight plus su cient tool obtainable within GIS environment which is applied broadly resolve multi criteria troubles, for instance, landslide susceptibility (Senouci et al, 2021). This technique including a combination of various factors considering its speci ed weights (Kaur et al, 2018). In this research, class rates and factor weights had been calculated with the analytic hierarchy processes.…”

“…Where; n is the number of factors (i.e. 9) and λ is the mean value of the reliability vector nd out in the above Table (14). λ = (12.37+12.44+11.62+11.58+4.87+10.66+10+8+10)/9 =10 Depend on the above equation, CI =10-9/9-1=0.13…”

Susceptibilidad a procesos de remoción en masa: Aproximación metodológica para la construcción de un índice de amenaza en Álvaro Obregón-Ciudad de México

“…Para determinar la susceptibilidad a PRM se realizó un análisis secundario de datos, bajo el método heurístico en combinación de elementos del Proceso Analítico Jerárquico-AHP, por sus siglas en inglés (Saaty, 1977), en el que los autores asignaron categorías a los factores causales, de acuerdo a su experiencia (Kaur, Gupta, Parkash, y Thapa, 2018;Paz Tenorio et al, 2017). Esta evaluación mixta de la susceptibilidad de PRM, se procesó usando la herramienta de "suma de rásteres", la cual agrega (suma) los valores de dos rásteres celda por celda, de la extensión de Spatyal Analyst, a través del Sistema de Información Geográfica ArcGIS 10.3 y el resultado, se comparó con un inventario de datos, basado en 196 eventos de PRM, atendidos y sistematizados por la Dirección de Protección Civil y Zonas de Alto Riesgo de AAO (Protección Civil, 2011).…”

“…Una limitante relevante y recurrente en este tipo de trabajos, corresponde a la diversidad de fuentes cartográficas, dadas las variaciones en las escalas de la cartografía temática, limitante reportada en los diferentes trabajos nacionales e internacionales (Kaur et al, 2018;Paz Tenorio et al, 2017;Reichenbach et al, 2018).…”

Landslide Susceptibility Assessment using Open-Source Data in the Far Western Nepal Himalaya: Case Studies from Selected Local Level Units

“…For larger areas with large number of landslide occurrence, data driven methods of LSM are useful, and for detailed site-specific study, physically based methods are useful that employs high resolution geotechnical and hydrological (Mergili et al, 2014). In knowledge driven method, the landslide conditioning factors are identified by the experts, and each factor are ranked or scored qualitatively to identify the importance of individual factor on the occurrence of landslide (Kaur et al, 2018;Sur et al, 2021) and thus the success of this method is dependent on the expert knowledge (Westen et al, 2003;Sur et al, 2020). Although data driven models using machine learning and physically based models in GIS are popular, they have limited applicability in data scarce areas like remote locations of Nepal.…”