Landslide susceptibility mapping of the Main Boundary Thrust (MBT) region in Tinau-Mathagadhi Section of Palpa District, Lumbini Province

Abstract

The study was carried out in Tinau-Mathagadhi section of Palpa District, Lumbini Province. The output of the landslide susceptibility analysis using frequency ratio (FR) is evaluated. Google Earth (CNES/Airbus and Maxar Technologies) Imagery of 50 cm spatial resolution was used to detect landslides. Data training and testing sampling was created using the landslide inventory. Eight causative factors were derived from topographic, geological, and land-use maps. The causative factors and training events were used to determine the FR ratings. The objective of present study is to address the effect of the Main Boundary Thrust (MBT) along with other important parameters in landslide susceptibility mapping. The integration of causative factors that assigned FR scores yielded the landslide susceptibility map. The validation of 79.6% was obtained using the ROC-AUC curve. Among eight causative factors, it was found that distance from the Main Boundary Thrust (MBT), aspect, land-use, and geology are dominating factors in the occurrence of the landslide. The FR method is based on a quantitative relationship between landslide inventory and landslide affecting factors. The method is valid for Tinau-Mathagadhi section and MBT is one of the causes of landslides in this area.

Landslide susceptibility mapping of the main boundary thrust region in Thungsingdanda-Bandipur section of Nawalparasi and Palpa Districts, Gandaki and Lumbini Provinces, Nepal

“…The weights derived through the frequency ratio method highlight the significant impact of terrain slope on landslide distribution. Generally, landslide occurrences increase with an increase in terrain gradient (Paudyal & Maharjan, 2022). However, in our study area, many landslides are concentrated in the range of 50 o -60 o, resulting in a highfrequency ratio of 1.73 and indicating heightened susceptibility to landslides (Table 1).…”

“…Over the past few decades, numerous scholars have developed effective techniques for producing precise landslide susceptibility maps. Examples of these approaches include frequency ratio (Goetz et al, 2015;Budha et al, 2016;Lee et al, 2016;Paudyal & Maharjan, 2022;(Neupane et al, 2023), logistic regression Steger et al, 2016); decision trees (Lee & Park, 2013;Pradhan, 2013;Tsangaratos & Ilia, 2016); fuzzy logic (Feizizadeh et al, 2014;Park et al, 2014;Pradhan, 2011), neurofuzzy systems (Pradhan, 2013;Aghdam et al, 2016;Lee et al, 2015); support vector machines (Pradhan, 2013;Peng et al, 2014;Lee et al, 2017;Tien Bui et al, 2017); artificial neural networks (Conforti et al, 2014;Pradhan & Lee, 2010;Tsangaratos & Benardos, 2014); and multimethod approaches (Althuwaynee et al, 2016;Pham et al, 2016;Pradhan, 2010;Yalcin et al, 2011). In this study, the effectiveness of the landslide susceptibility assessment was evaluated using the frequency ratio (FR) method.…”

Landslide susceptibility mapping of the Main Boundary Thrust region in Mandre - Khursanibari section of Arghakhanchi and Palpa Districts, Lumbini Province, Nepal

“…Their instability primarily stems from the mountains' rocks having a naturally weak geological setting. The main potential sites of landslides in the Nepal Himalayas are dictated by adverse geological conditions like the presence of thrusts, faults, and adverse lithology in addition to other triggering factors like steep slope, concentrated precipitation, and seismic activity (Acharya & Paudyal, 2023;Paudyal & Maharjan, 2022;Dhahal & Paudyal, 2022;Neupane & Paudyal, 2021;K.C. et al, 2018;Budha et al, 2016).…”

“…Nepal's hilly areas, with their rough landscapes, frequent earthquakes, and heavy rains, are vulnerable to various natural dangers. Landslides can harm the region's growth over time (Paudyal & Maharjan, 2022).…”