English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 57517/91034 (63%)
造訪人次 : 13466370      線上人數 : 339
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: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/67804

    題名: Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network
    作者: Chang, Fi-John;Chang, Li-Chiu;Kao, Huey-Shan;Wu, Gwo-Ru
    貢獻者: 淡江大學水資源及環境工程學系
    關鍵詞: Artificial neural network;Evaporation;Meteorological variables;Self-organizing map
    日期: 2010-04
    上傳時間: 2011-10-23 02:04:35 (UTC+8)
    出版者: Amsterdam: Elsevier BV
    摘要: The phenomenon of evaporation affects the distribution of water in the hydrological cycle and plays a key role in agriculture and water resource management. We propose a self-organizing map neural network (SOMN) to assess the variability of daily evaporation based on meteorological variables. The daily meteorological data sets from a climate gauge were collected as inputs to the SOMN and then were classified into a topology map based on their similarities to investigate their multi-collinear relationships to assess their effort in the evaporation. To accurately estimate the daily evaporation based on the input pattern, the weights that connect the clustered centers in a hidden layer with the output were trained by using the least square regression method. In addition, we compared the results with those of back propagation neural network (BPNN), modified Penman and Penman–Monteith formulas. The results demonstrated that the topological structures of SOMN could give a meaningful map to present the clusters of meteorological variables and the networks could well estimate the daily evaporation. By comparing the performances of these models in estimating daily and long-term (monthly or yearly) cumulative evaporation, the SOMN provides the best performance.
    關聯: Journal of Hydrology 384(1–2), pp.118–129
    DOI: 10.1016/j.jhydrol.2010.01.016
    顯示於類別:[水資源及環境工程學系暨研究所] 期刊論文


    檔案 大小格式瀏覽次數



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