基于多时空滑坡编录和TrAdaBoost迁移学习的滑坡易发性评价

付智勇; 李典庆; 王顺; 杜文琪

doi:10.3799/dqkx.2023.013

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1935-1947

Fu Zhiyong, Li Dianqing, Wang Shun, Du Wenqi, 2023. Landslide Susceptibility Assessment Based on Multitemporal Landslide Inventories and TrAdaBoost Transfer Learning. Earth Science, 48(5): 1935-1947. doi: 10.3799/dqkx.2023.013

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Fu Zhiyong, Li Dianqing, Wang Shun, Du Wenqi, 2023. Landslide Susceptibility Assessment Based on Multitemporal Landslide Inventories and TrAdaBoost Transfer Learning. Earth Science, 48(5): 1935-1947. doi: 10.3799/dqkx.2023.013

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Landslide Susceptibility Assessment Based on Multitemporal Landslide Inventories and TrAdaBoost Transfer Learning

doi: 10.3799/dqkx.2023.013

State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China

Received Date: 2022-11-01
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

To overcome the shortcoming of insufficient landslide inventories, TrAdaboost-DT and TrAdaBoost-RF models with decision tree and random forest as basic learners respectively in 2013-2015 were built, by taking the Wenchuan-Yingxiu, Sichuan Province as the study area and the landslide inventory in 2011-2013 as an auxiliary data set. The proposed models were used to predict landslide susceptibility and prediction results were compared with those of DT and RF models trained by the landslide inventory in 2013-2015. The comparison results show that areas of under receiver operating characteristic curve (AUC) of TrAdaBoost-DT and TrAdaBoost-RF models were more than 0.03 and 0.01 than those of DT and RF models, respectively. To validate the prediction performance of the proposed models, the landslide inventory in 2013-2015 was used to build LS model in 2015-2018. The results indicate that the AUC of both DT and RF models increased by 0.13 using the proposed model. TrAdaBoost algorithm can improve the prediction performance of LS model based on machine learning algorithm and show significant improvement for those under small data sets.
- landslide susceptibility,
- landslide inventory,
- transfer learning,
- TrAdaBoost,
- decision tree,
- random forest,
- engineering geology

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Landslide Susceptibility Assessment Based on Multitemporal Landslide Inventories and TrAdaBoost Transfer Learning

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