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    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

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