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    Slope Collapse Prediction Using Bayesian Framework with K-Nearest Neighbor Density Estimation: Case Study in Taiwan

    Source: Journal of Computing in Civil Engineering:;2016:;Volume ( 030 ):;issue: 001
    Author:
    Min-Yuan Cheng
    ,
    Nhat-Duc Hoang
    DOI: 10.1061/(ASCE)CP.1943-5487.0000456
    Publisher: American Society of Civil Engineers
    Abstract: Slope failures across mountain roads can damage man-made structures, interrupt traffic, and give rise to fatal accidents. Disastrous consequences of these hazards necessitate the approach for predicting their occurrences. In practice, slope collapse prediction can be formulated as a classification problem with two class labels:
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      Slope Collapse Prediction Using Bayesian Framework with K-Nearest Neighbor Density Estimation: Case Study in Taiwan

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    http://yetl.yabesh.ir/yetl1/handle/yetl/72243
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    contributor authorMin-Yuan Cheng
    contributor authorNhat-Duc Hoang
    date accessioned2017-05-08T22:08:43Z
    date available2017-05-08T22:08:43Z
    date copyrightJanuary 2016
    date issued2016
    identifier other33034175.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/72243
    description abstractSlope failures across mountain roads can damage man-made structures, interrupt traffic, and give rise to fatal accidents. Disastrous consequences of these hazards necessitate the approach for predicting their occurrences. In practice, slope collapse prediction can be formulated as a classification problem with two class labels:
    publisherAmerican Society of Civil Engineers
    titleSlope Collapse Prediction Using Bayesian Framework with K-Nearest Neighbor Density Estimation: Case Study in Taiwan
    typeJournal Paper
    journal volume30
    journal issue1
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000456
    treeJournal of Computing in Civil Engineering:;2016:;Volume ( 030 ):;issue: 001
    contenttypeFulltext
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