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In the mountainous region of the world, landslides are a major issue that significantly impacts the socioeconomic, infrastructure, livestock, and human lives. A landslide susceptibility map (LSM) identifies potential areas of landslide. The preparation of the LSM and the accuracy are both impacted by the training and testing datasets. Therefore, this study assesses the impact of various landslide sampling methods for LSM preparation as well as understanding the impact of the presence or absence of non-landslide points on its accuracy. Total 898 landslide points were divided into training and testing landslide points using random sampling, grid sampling, and stratified sampling (based on elevation, slope, and watershed). The training landslide points and the 10 causative factors are used for the preparation of LSM using frequency ratio (FR), information value (IoV), and multiple linear regression (MLR) methods. The …