2023
DOI: 10.3126/gjn.v16i01.53486
|View full text |Cite
|
Sign up to set email alerts
|

Landslide susceptibility analysis using frequency ratio and weight of evidence approaches along the Lakhandehi Khola watershed in the Sarlahi District, southern Nepal

Abstract: Landslide susceptibility maps are considered as one of the most important keys to limiting and dodging potential landslide consequences worldwide. In the present study, landslide susceptibility maps are prepared using bivariate models: frequency ratio and weight of evidence approaches. At first, randomly selected 80% of landslides i.e.,one hundred eighty landslides are used as training data for the preparation of the model, and the rest 20% of landslides i.e.,forty-five landslides for its validation. Similarly… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
2
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 19 publications
0
2
0
Order By: Relevance
“…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.…”
Section: Methodsmentioning
confidence: 99%
“…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.…”
Section: Methodsmentioning
confidence: 99%
“…Statistical models aim to reduce subjectivity inherent in qualitative models by providing a more objective and reproducible weight of factors. These models can be either bivariate or multivariate, with common methods including the weight of evidence model (Batar & Watanabe, 2021), frequency ratio model (Neupane et al, 2023) and fuzzy logic model (Pourghasemi, Pradhan, & Gokceoglu, 2012) for bivariate analysis and logistic regression analysis (Ayalew & Yamagishi, 2005) and the artificial neural network method (Lee et al, 2003(Lee et al, , 2004 for multivariate analysis. In recent years, ML methods have gained traction in landslide susceptibility studies (Merghadi et al, 2020;Sun et al, 2020), with commonly used algorithms including support vector machine, decision tree and Random Forest (RF; Hong et al, 2018;Peng, Niu, et al, 2014;Zhou et al, 2021).…”
mentioning
confidence: 99%