https://scholars.lib.ntu.edu.tw/handle/123456789/598479
標題: | Integrating InSAR observables and multiple geological factors for landslide susceptibility assessment | 作者: | Lin Y.-T Chen Y.-K Yang K.-H Chen C.-S JEN-YU HAN KUO-HSIN YANG |
關鍵字: | InSAR;Landslide potential;Machine learning;Slope unite;Spatial factors | 公開日期: | 2021 | 卷: | 11 | 期: | 16 | 來源出版物: | Applied Sciences (Switzerland) | 摘要: | Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technol-ogy, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model out-performed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112332588&doi=10.3390%2fapp11167289&partnerID=40&md5=02b55b5ec8529a986798e98120e32526 https://scholars.lib.ntu.edu.tw/handle/123456789/598479 |
ISSN: | 20763417 | DOI: | 10.3390/app11167289 |
顯示於: | 土木工程學系 |
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