ArticleScientific reports2024
Effects of non-landslide sampling strategies on machine learning models in landslide susceptibility mapping.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 55 citations in OpenAlex.
- Assessment of landslide hazard under specific rainfall scenarios based on a hybrid CNN-TRIGRS and matrix approach: a case study of Meishan Town, Zhejiang Province, China.Scientific reports · 2026Article
- A Review of High-Throughput Optical Sensors for Food Detection Based on Machine Learning.Foods (Basel, Switzerland) · 2026Review
- Snow avalanche susceptibility, hazard, and exposure assessment in the Western Himalaya using machine learning and numerical modelling.Scientific reports · 2025Article
- Landslide susceptibility assessment using lightweight dense residual network with emphasis on deep spatial features.Scientific reports · 2025Article
- Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
This study aims to explore the effects of different non-landslide sampling strategies on machine learning models in landslide susceptibility mapping. Non-landslide samples are inherently uncertain, and the selection of non-landslide samples may suffer from issues such as noisy or insufficient regional representations, which can affect the accuracy of the results. In this study, a positive-unlabeled (PU) bagging semi-supervised learning method was introduced for non-landslide sample selection. In addition, buffer control sampling (BCS) and K-means (KM) clustering were applied for comparative analysis. Based on landslide data from Qiaojia County, Yunnan Province, China, collected in 2014, three machine learning models, namely, random forest, support vector machine, and CatBoost, were used for landslide susceptibility mapping. The results show that the quality of samples selected using different non-landslide sampling strategies varies significantly. Overall, the quality of non-landslide samples selected using the PU bagging method is superior, and this method performs best when combined with CatBoost for predicting (AUC = 0.897) landslides in very high and high susceptibility zones (82.14%). Additionally, the KM results indicated overfitting, displaying high accuracy for validation but poor statistical outcomes for zoning. The BCS results were the worst.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.