English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62805/95882 (66%)
造訪人次 : 3979299      線上人數 : 384
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120225


    題名: Explore training self-organizing map methods for clustering high-dimensional flood inundation maps
    作者: Li-Chiu Chang;Wu-Han Wang;Fi-John Chang
    關鍵詞: Self-Organizing Map (SOM);Flood inundation map;Topological map;Artificial Intelligence (AI)
    日期: 2020-10-18
    上傳時間: 2021-03-17 12:11:33 (UTC+8)
    摘要: The Self-Organizing Map (SOM) can supportively organize complex datasets such as highly dimensional flood inundation maps. Nevertheless, SOM may produce distinct patterns after being trained with identical samples or may not converge in clustering highly dimensional datasets, which causes usability concerns and prevents its applications from a broader spectrum. Motivated by such concerns, two training strategies (S1 and S2) were proposed to configure SOM based on a large number of highly dimensional flood inundation maps associated with two basins located in southern Taiwan. S1 focused mainly on the weights’ adjustments in the ordering stage, while S2 would methodically balance the ordering and convergence activities on the weights’ adjustments. The effectiveness and suitability of S1 and S2 were inspected in detail by using coverage ratio, flip detector, and five clustering indices based on their configured topological maps in the two basins. The clustering results showed that the flip detector and the coverage ratio could visibly and objectively examine the suitability of the configured topological map. It was noticed that the influences of the ordering and convergence stages upon both training strategies for building SOM could significantly affect the coverage ratio as well as flip condition. Comparing the SOM topological maps implemented separately with each strategy, S2 strategy has a lower probability of causing a flipping situation and takes far fewer iterations to train a model of the same network size, which indicates S2 is more efficient and effective than S1 in configuring the SOM topological map for representing regional flood inundation maps.
    關聯: Journal of Hydrology 595, 125655
    DOI: 10.1016/j.jhydrol.2020.125655
    顯示於類別:[人工智慧學系] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    Explore training self-organizing map methods for clustering high-dimensional flood inundation maps.pdf5280KbAdobe PDF3檢視/開啟
    index.html0KbHTML80檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋