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Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm

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Abstract

The quality of the sample plays a vital role in developing accurate models using machine learning. This aspect is equally important when evaluating regional landslide susceptibility using machine learning. Previous studies have mostly employed random generation methods to select samples, which often fail to select representative samples. Therefore, this study proposes the KK-sampling method, which uses K-means and KNN algorithms to analyze relevant attributes of the study area and select samples. To evaluate the effectiveness of the proposed method, this study employed MLP, RF, and XGBoost models in conjunction with the KK-sampling method, with Zhong County, Chongqing serving as a case study. The results indicate that the KK-sampling method significantly improves the stability and accuracy of the model. Additionally, this study analyzed the importance of landslide factors in Zhong County using SHAP values. The findings provide a reference for establishing a reasonable and effective landslide susceptibility model in the region.

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Data availability

The datasets analysed during the current study are available in the following table.

Factor

Source

Web link to datasets

elevation

The Earth Science Data Systems (ESDS) Program provided by NASA’s collection of Earth science data

https://search.asf.alaska.edu/#/

NDVI

Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences

https://www.gscloud.cn

Lithology

China National Digital Geological Map (Public Version) Spatial Database

http://dcc.ngac.org.cn/

River

Vector Map (Public Version) from National Catalogue Service For Geographic Information

https://www.webmap.cn/

Rain

National Earth System Science Data Center, National Science & Technology Infrastructure of China

http://www.geodata.cn

Population

WorldPop, University of Southampton, UK

https://hub.worldpop.org/

Soil

Harmonized World Soil Database Version (HWSD)

https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/

Erosion

Resource & Environment Science & Data Center of Academia Sinica

 

Road

Vector Map (Public Version) from National Catalogue Service For Geographic Information

https://www.webmap.cn/

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Acknowledgements

The authors would like to thank Geoscientific Data & Discovery Publishing System for providing data sharing. The author is grateful to Dr. Huang Jian for his help with the skills of writing in this paper. The author thanks the associate editor and the reviewers for their useful feedback that improved this paper.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, Sichuan, China

    Chao Liu

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Author 1 (Chao Liu): Conceptualization, Data Curation, Methodology, Software, Investigation, Formal Analysis, Writing—Review & Editing.

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Highlights

• Utilize the K-means-KNN (KK) algorithm to enhance the stability and accuracy of landslide susceptibility mapping through machine learning.

• Evaluate and compare landslide susceptibility in Zhong County, Chongqing, using three sampling methods (random sampling, buffer sampling, and KK-sampling) and three models (Multilayer Perception, Random Forest, XGBoost).

• After applying the SHAP method based on Game theory to analyze the model in this study, it reveals that road, soil type, and elevation are the most influential factors.

• On average, KK-sampling demonstrated superior performance compared to random sampling and buffer sampling, resulting in an increase of 12.3% in accuracy, 42% in F1 score, and 20.1% in AUC.

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Liu, C. Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm. Earth Sci Inform 16, 4131–4152 (2023). https://doi.org/10.1007/s12145-023-01151-z

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