© 2020 Agriculture and Forestry Journal This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial 3.0 International License        Vol. 4, Issue 2, pp. 80-90, December, 2020 http://ojs.univ-tlemcen.dz/index.php/AFJ/ E-ISSN 2602-5795 Published by university of Tlemcen - ALGERIA Analytical hierarchy process (AHP) based landslide susceptibility mapping of Kaligandaki hydro-catchment of Syangja, Nepal Dhan Bahadur BIST 1* , Mahendra Singh THAPA 2 , Urmila BISTA 3 , Nripesh AWASTHI 1 1 Ministry of Industry, Tourism, Forests and Environment, Sudurpaschim Province, Dhangadhi, Nepal 2 Institute of Forestry, Pokhara Campus, Tribhuvan University, Pokhara, Nepal 3 Central Department of Botany, Tribhuvan University, Kirtipur, Kathmandu, Nepal *Corresponding author: roshanbist1234@gmail.com ARTICLE INFO ABSTRACT Article history: Received: 20 April 2020 Accepted after corrections : 23 August 2020 Keywords: Landslide hazard, AHP, Geology, Susceptibility, GIS. Landslide problems are abundant in the mountainous areas of Nepal. This study aims to prepare landslide susceptibility map (LSM)using Analytical Hierarchy Process (AHP) method of the Kaligandaki hydro-catchment, Syangja, Nepal. Eight factor maps viz. slope, aspect, distance to stream/river, lithology, distance to faults, precipitation, land use and distance to road were used for preparing thematic layers. Weight for each factor was assigned using AHP depending on its influence on the landslide occurrence. The LSM was obtained with combination of weighted thematic layers and reclassified into five susceptible classes namely, very low (VL), low (L), moderate (M), high (H) and very high(VH).Altogether 27 landslide incidents were recorded by inventory approach. The result showed that about 40% of the study area is highly susceptible for landslide occurrence. The study revealed that higher slope (>30o) with combination of lithological factor has higher effect on landslide occurrence. Similarly, west facing slopes were found to be more susceptible to landslide occurrence in comparison to other aspects. The majority of the landslides were found near proximity of roads and streams/rivers. Finally, the landslide hazard zonation map was crossed with the landslide distribution map and the model applicability was confirmed by determining the per hazard class percent of area covered by the landslide. Further, the effectiveness of the map was also confirmed by the high statistically significant value of a chi-square test. The LSM can be useful for the decision-makers and planners in choosing suitable locations for the development works like road network, drainage network, drinking water, etc. 1. Introduction Landslide is the most damaging geological disaster all around the world causing loss of lives and damage to both man-made and natural structures (Petley et al., 2007; Froude & Petley, 2018).Nepal falls in tectonically most active zones on earth at the center of 2400km long Himalayan mountain range(Petley et al., 2007). Since this region is tectonically very unstable with rugged topography, unstable geological structures, soft and fragile rocks, common earthquakes, along with heavy and prolonged rainfalls during monsoon periods (Devkota et al., 2013), landslides holds significant phenomena among the various land degradation process prevalent in the country (Ahmad & Joshi, 2010). In Nepal, landslide disaster has been accelerated because of the impact of artificial structures and human interventions on mountain slopes followed by expansion of agricultural land, large scale deforestation, unplanned settlements and infrastructure developments as rural roads, hydro-dams, irrigation canals and so on without considering proper engineering plans, geological and geographical investigation (Rajbhandari et al., 2002). Deeply weathered and fractured rocks and greatly incised rivers and streams contribute to excessive mass wasting in the mountainous terrain. Besides, high precipitation during the monsoon season (June–September) is another detrimental factor, which causes landslips, debris flows and flash floods. Consequently, natural and man-made disasters are increasing, which are often resulting in substantial economic and environmental losses and causing a great suffering to many people (Ministry of Home Affairs, 2018). In this context, the identification of probable landslide hazard zone and an early prediction of such events might be boon for saving millions of life properties and, mitigating the impacts or situation from being worse and serious. Several methods have been used for assessing landslide susceptibility mapping, hazard mapping and risk evaluation (Chauhan et al., 2010; Kayastha et al., 2013; Pardeshi et al., 2013). Inventory, historical records, satellite images and aerial photo interpretation have helped Experts to evaluate inducing factors, and identify sites that have similar geological and geomorphological feature (El Jazouli et al., 2019). Generally, the methods applied for landslide susceptibility mapping can be divided into (1) qualitative method (Kayastha et al., 2013; Sharma and Mahajan, 2018) (2) quantitative method including bivariate and multivariate modeling methods for statistical evaluation of landslides occurrences (Devkota et al., 2013; Sharma and Mahajan, 2018; El Jazouli et al., 2019).Qualitative models are the simplest methods which are entirely based on the expert knowledge and experiences of the persons carrying out the susceptibility or hazard assessment (Kaur et al., 2017).With