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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Kao, JJSubject: search.filters.subject.drainage pattern

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Now showing 1 - 3 of 3
  • Thumbnail Image
    Item type:Item,
    NPS model parameter uncertainty analysis for an off-stream reservoir
    (1996-10-01)
    Kao, JJ
    ;
    Hong, HJ
    ;
    環境工程研究所
    ;
    Institute of Environmental Engineering
    Parameter uncertainties exert a significant effect on nonpoint source pollution (NPS) modeling results. A decision made on the basis of such results may thereby be inappropriate. In this work, the parameter uncertainty is analyzed to explore an improved modeling procedure. Drainage patterns generated from digital elevation data and rainfall are the major parameters examined. A case study for the watershed of the Posan off-stream reservoir is implemented. A significant spatial variation of NPS distribution simulated with a drainage pattern generated from varied methods is observed. The effects of rainfall randomness on the spatial loading distribution are assessed and computed based on a Monte Carlo simulation. The proposed procedure is capable of improving the quality of modeling results and the decision for an appropriate control strategy.
  • Thumbnail Image
    Item type:Item,
    NPS model parameter uncertainty analysis for an off-stream reservoir
    (1996-10-01)
    Kao, JJ
    ;
    Hong, HJ
    ;
    交大名義發表
    ;
    環境工程研究所
    ;
    National Chiao Tung University
    Parameter uncertainties exert a significant effect on nonpoint source pollution (NPS) modeling results. A decision made on the basis of such results may thereby be inappropriate. In this work, the parameter uncertainty is analyzed to explore an improved modeling procedure. Drainage patterns generated from digital elevation data and rainfall are the major parameters examined. A case study for the watershed of the Posan off-stream reservoir is implemented. A significant spatial variation of NPS distribution simulated with a drainage pattern generated from varied methods is observed. The effects of rainfall randomness on the spatial loading distribution are assessed and computed based on a Monte Carlo simulation. The proposed procedure is capable of improving the quality of modeling results and the decision for an appropriate control strategy.
  • Thumbnail Image
    Item type:Item,
    A Xerion-based Perl program to train a neural network for grid pattern recognition
    (1996-11-01)
    Kao, JJ
    ;
    交大名義發表
    ;
    環境工程研究所
    ;
    National Chiao Tung University
    ;
    Institute of Environmental Engineering
    Neural network training with the back propagation algorithm is an important artificial intelligence technique for grid pattern recognition. The training is time-consuming however and generally requires a trial-and-error procedure to configure the network. A Perl program executed with Xerion is presented to relieve the;training burden. Statistical reports such as computation time, learning performance, and validation performance are generated automatically by the program. A case study applying the program for training networks to determine a drainage pattern from Digital Elevation Model data is demonstrated and discussed. Manually determining drainage patterns from topographical maps for a grid-based model is tedious and subjective. The neural network has a self-learning capability that can replace human judgment involved in the conventional approach. Copyright (C) 1996 Elsevier Science Ltd.