Landslide Susceptibility Mapping Based on Resampling Method and FR-CNN: A Case Study of Changdu

Zili Qin, Xinyao Zhou, Mengyao Li, Yuanxin Tong and Hongxia Luo ()
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Zili Qin: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Xinyao Zhou: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Mengyao Li: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Yuanxin Tong: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Hongxia Luo: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China

Land, 2023, vol. 12, issue 6, 1-20

Abstract: Deep learning can extract complex and high-dimensional characteristic information with its deep structure, effectively exploring the complex relationship between landslides and their numerous influencing factors, and ultimately, more accurately predict future landslide disasters. This study builds a landslide susceptibility mapping (LSM) method based on deep learning, compares the frequency ratio (FR) sampling method with a buffer random sampling method, and performs resampling operations of landslide and non-landslide samples to explore the applicability of deep learning in LSM. In addition, six indices, precision, accuracy, recall, ROC, and the harmonic mean F1 of accuracy and recall were selected for quantitative comparison. The results show that both the resampling method proposed in this paper and the non-landslide sample selection method based on FR can significantly improve the accuracy of the model, with the area under curve (AUC) increasing by 1.34–8.82% and 3.98–7.20%, respectively, and the AUC value can be improved by 5.32–9.66% by combining the FR selection and resampling methods. Furthermore, all the deep learning models constructed in this study can obtain accurate and reliable landslide susceptibility analysis results compared to traditional models.

Keywords: landslide susceptibility analysis; deep learning; frequency ratio; Qinghai–Tibetan Plateau (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:6:p:1213-:d:1168667

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