English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49378/84106 (59%)
Visitors : 7372954      Online Users : 64
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/67804


    Title: Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network
    Authors: Chang, Fi-John;Chang, Li-Chiu;Kao, Huey-Shan;Wu, Gwo-Ru
    Contributors: 淡江大學水資源及環境工程學系
    Keywords: Artificial neural network;Evaporation;Meteorological variables;Self-organizing map
    Date: 2010-04
    Issue Date: 2011-10-23 02:04:35 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: 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.
    Relation: Journal of Hydrology 384(1–2), pp.118–129
    DOI: 10.1016/j.jhydrol.2010.01.016
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML208View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


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