Abstract
In this paper we report our results from analysing a hybrid spatial multi-criteria evaluation (SMCE) method for generating landslide susceptibility mapping (LSM). This study is the first of its kind in the Kullu valley, Himalayas. We used eight related geospatial conditioning factors from three main groups: geological, morphological and topographical factors. Our landslide inventory dataset has a total of 149 GPS points of landslide locations, collected based on a field survey in July 2018. The relationships between landslide locations and conditioning factors were determined using the GIS-based statistical methods of frequency ratio (FR), multi-criteria decision-making (MCDM) and the integration method of hybrid SMCE. We compared the performance of applied methods by dividing the inventory into testing (70%) and validation (30%) datasets. The area under the curve (AUC) was used to validate the results. The integration method of hybrid SMCE gave the highest accuracy rate (0.910) compared to the other two methods, with 0.797 and 0.907 accuracy rates for the analytical hierarchy process (AHP) and FR, respectively. The applied methodologies are easily transferable to other areas, and the resulting landslide susceptibility maps (LSMs) can be useful for risk mitigation and development planning purposes in the Kullu valley, Himalayas.
Highlights
Landslides are among the most damaging geological hazards in mountainous regions such as the Himalayas
We present a synoptic assessment of landslide susceptibility assessments and GIS-based statistical methods in a comparative study of the analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) methods for creating landslide susceptibility mapping (LSM) in the Kullu valley along the Larji–Kullu tectonic window (LKTW) zone in the higher Himalayas
The hybrid SMCE method is applied as an integration of the FR and AHP
Summary
Landslides are among the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan orogeny, which is tectonically the most active mountainous region in the world, is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is an effective tool for understanding the probability of the spatial distribution of future landslides [2]. GIS-based MCDM is an important geospatial analysis method which combines geospatial and non-spatial data to produce LSMs of an area [4]. The GIS tool integrated with MCDM methods provides a geospatial framework to organise these various thematic layers into a hierarchical structure and examine the relationships between the different geospatial components [5]. Landslide conditioning factors have been analysed to map susceptible areas in several mountainous regions around the world since the early 1980s [6,7]. Nowadays with growing geo-computation, there are new methods like automatic and semi-automatic computation for LSM and Geosciences 2019, 9, 156; doi:10.3390/geosciences9040156 www.mdpi.com/journal/geosciences
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