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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
PDF(9351 KB)
PDF(9351 KB)
基于多时空滑坡编录和TrAdaBoost迁移学习的滑坡易发性评价
Landslide Susceptibility Assessment Based on Multitemporal Landslide Inventories and TrAdaBoost Transfer Learning
为了解决震区不同时期易发性评价中滑坡编录样本不足问题，以汶川地震震区汶川‒映秀区域为研究区，基于TrAdaBoost迁移学习算法，利用2011‒2013年滑坡数据集辅助训练2013‒2015年滑坡数据集的滑坡易发性模型，分别建立了以决策树（DT）和随机森林（RF）为单体学习器的TrAdaBoost-DT和TrAdaBoost-RF滑坡易发性模型.通过所建立的模型对研究区的滑坡易发性进行预测，并将预测结果与仅用2013‒2015年滑坡数据集所建立的DT和RF模型的预测结果进行对比.以受试者工作曲线下方面积（AUC）为评价指标，TrAdaBoost模型使得DT和RF模型的AUC分别提高了0.03和0.01.为了进一步验证所提方法有效性，以2013‒2015年滑坡数据集辅助训练2015‒2018年滑坡数据集中的易发性模型.结果表明，基于TrAdaBoost模型优化DT和RF模型的AUC均提高了0.13；TrAdaBoost模型能够有效提高传统基于机器学习滑坡易发性模型的预测性能，且对小数据集下的滑坡易发性模型的预测性能提升更为显著.
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.
滑坡易发性 / 滑坡编录 / 迁移学习 / TrAdaBoost / 决策树 / 随机森林 / 工程地质
landslide susceptibility / landslide inventory / transfer learning / TrAdaBoost / decision tree / random forest / engineering geology
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