Sequent data mining analysis for rainfall-based landslide events with the refinement of landslide samples and feature reduction

Jhe Syuan Lai, Fuan Tsai, Jing Hung Hwang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

The objective of this study is to adopt the Bayesian network algorithm to predict sequent rainfall-induced landslides of Shimen reservoir watershed in Taiwan since 2004 to 2008. Previous landslide events are used as training data to classify the samples of next event. Two subjects are further explored in this study. The first is eliminating landslide runout from landslide samples, and the other is feature (variable) reduction. The former is performed in order to refine landslide samples for prediction improvement, because image-based interpretation cannot discriminate landslide and landslide runout area. The latter is to reduce redundancy of landslide events and variables. Experimental results demonstrate that the landslide runout elimination and feature reduction can improve the prediction accuracy and the computation efficiency while maintaining acceptable results in landslide detection and prediction.

原文???core.languages.en_GB???
主出版物標題34th Asian Conference on Remote Sensing 2013, ACRS 2013
發行者Asian Association on Remote Sensing
頁面3629-3636
頁數8
ISBN(列印)9781629939100
出版狀態已出版 - 2013
事件34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
持續時間: 20 10月 201324 10月 2013

出版系列

名字34th Asian Conference on Remote Sensing 2013, ACRS 2013
4

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???event.eventtypes.event.conference???34th Asian Conference on Remote Sensing 2013, ACRS 2013
國家/地區Indonesia
城市Bali
期間20/10/1324/10/13

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