Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya

Abstract

Landslides in Himalaya cause widespread damage in terms of property and human lives. It the present study, an attempt is made to derive information on causative parameters and preparation of landslide-susceptible map using fuzzy data integration in one of the seismically active region of Garhwal Himalaya that was recently devastated by a huge landslide. High-resolution remotely sensed data products acquired from Indian Remote Sensing Satellite before and after the landslide event were processed to improve interpretability and derivation of causative parameters. Spatial data sets such as lithology, rock weathering, geomorphology, lineaments, drainage, land use, anthropogenic factor, soil type and depth, slope gradient, and slope aspect were integrated using fuzzy gamma operator. The final map was reclassified in to five classes such as highly to lowly susceptible classes based on cumulative cutoff. The result shows around 72% of known landslide areas including the large Uttarkashi landslide in the high and very high susceptibility classes comprising of only 37% of the total area. The precipitation data from ground-and satellite-based observations were compared; the precipitation threshold and the role of seismic activity were analyzed for initiation of landslide.

Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland

“…It shows the spatial distribution of landslide-prone areas, usually as landslide occurrence probabilities distributed across grid cells (Goetz et al 2015). Different methods of LSM have been broadly examined and analysed in the past decades Goetz et al 2015;Bai et al 2010;Mashari et al 2012;Tien Bui et al 2011, Feizizadeh et al 2014Dimri et al 2007;Kanungo et al 2008). Moreover, numerous comparisons of LSM methods have been evaluated and still no single best method has been selected (Goetz et al 2015).…”

“…Due to this fact, the weights assignment is getting more complicated and time-consuming if the quantity of LCF increases. Except for statistical methods, data mining using fuzzy logic (Feizizadeh et al 2014;Dimri et al 2007;Kanungo et al 2008) and artificial neural network models (Ermini et al 2005;Lee and Evangelista 2006;Kanungo et al 2006) have also been applied to the LSM using GIS. Furthermore, applications of data mining and soft computing methods are increasing rapidly.…”

Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim

“…Fuzzy membership function can be determined subjectively or objectively. There is no universal approach available for the determination of fuzzy membership function (Champatiray et al 2007). A suitable and universally acceptable approach may enhance information accuracy (prediction capability).…”

“…LHEF (Landslide Hazard Evaluation Factor) based LHZ was carried out by Anbalagan (1992), Landslide hazard mapping based on geological attributes (Pachauri and Pant 1992), GIS based landslide hazard zonation (Gupta et al 1999), integrated approach for landslide hazard zonation (Sarkar and Kanungo 2004) and GISbased statistical landslide susceptibility zonation (Saha et al 2005). Some authors adopted other techniques namely landslide hazard zonation based on meso scale for town planning (Anbalagan et al 2008), fuzzy logic based LSZ mapping (Kanungo et al 2006;Champatiray et al 2007), predictive modeling of landslide hazard in lesser Himalaya by Dahal et al (2008). Several quantitative and semiquantitative techniques were applied for landslide susceptibility/hazard modelling in Himalayan terrain.…”

Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India

“…The fuzzy gamma operation (at g ¼ 0.9) in landslide hazard zonation in the Kakan catchment area in southwest Iran was applied by Tangestani (2004) and g ¼ 1 by Kanungo et al (2009) in the Darjeeling area of eastern Himalaya. Champatiray et al (2007) also successfully used the fuzzy gamma operation for landslide hazard assessment in the Uttarkashi region of the Himalaya. In the present work, therefore, the Fuzzy Gamma Operator was experimented with, changing the gamma (g) values in the range of 0 and 1 using the following formula: where g is a parameter in the range 0 to 1, FAS is the fuzzy algebraic sum and FAP is the fuzzy algebraic product.…”