气候变化领域集成服务门户(CCPortal): Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet

CCPortal

DOI	10.3390/app13127276
	Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet
	Liang, Zhu; Peng, Weiping; Liu, Wei; Huang, Houzan; Huang, Jiaming; Lou, Kangming; Liu, Guochao; Jiang, Kaihua
发表日期	2023
EISSN	2076-3417
卷号	13 期号:12
英文摘要	Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost) and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong county, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (AUC 0.974 and accuracy=93.3%), while the LR model performed the worst (AUC 0.974 and accuracy=93.3%) and CatBoost model performed better (AUC 0.974 and accuracy=93.3%). Besides, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, did a more detailed analysis of conditioning factors but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.
关键词	Landslide Susceptibility Information Value Logistic regression Machine learning Deep learning GIS
英文关键词	LOGISTIC-REGRESSION; FREQUENCY RATIO; QUANTITATIVE-ANALYSIS; MACHINE; AREA; GIS; HAZARD; BASIN
WOS研究方向	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001016946000001
来源期刊	APPLIED SCIENCES-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283543
作者单位	Jilin University
推荐引用方式 GB/T 7714	Liang, Zhu,Peng, Weiping,Liu, Wei,et al. Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet[J],2023,13(12).
APA	Liang, Zhu.,Peng, Weiping.,Liu, Wei.,Huang, Houzan.,Huang, Jiaming.,...&Jiang, Kaihua.(2023).Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet.APPLIED SCIENCES-BASEL,13(12).
MLA	Liang, Zhu,et al."Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet".APPLIED SCIENCES-BASEL 13.12(2023).