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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.Chang, Liang-ChengSubject: search.filters.subject.artificial neural network

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    Developing a Conjunctive Use Optimization Model for Allocating Surface and Subsurface Water in an Off-Stream Artificial Lake System
    (2016-08-01)
    Pan, Chen-Che
    ;
    Chen, Yu-Wen
    ;
    Chang, Liang-Cheng
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    Huang, Chun-Wei
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    土木工程學系
    This work develops a rule curve-based conjunctive use management model for optimizing the operating rules for a lake-groundwater system with off-stream storage lakes. The proposed procedure is a simulation-optimization approach that embeds an Artificial Neural Network ( ANN) instead of a groundwater numerical model into a genetic algorithm ( GA). The direct physical exchange between lake water with groundwater is simulated using the ANN model, which is a reduced version of a full numerical model, MODFLOW with an LAK3 module. By applying the ANN model, the proposed procedure can reduce the computational burden that is induced by the nonlinear exchange. An operating rule-based optimal conjunctive use management model for the Gaopin Artificial lakes system in Taiwan was thus developed using the proposed framework. A set of optimal solutions involves rule curves and a discount ratio. Simulation results demonstrate that the embedded ANN model can accurately simulate the nonlinear exchange of a lake with groundwater. The embedded ANN model is less computationally complex than the numerical model. This work demonstrates a methodology for reducing the computational burden of the optimal conjunctive use management model that is associated with an internal nonlinear system by using the ANN reduced model. Specifically, the concept of, and results obtained using the developed operating rule-based model incorporating five artificial lakes and considering the nonlinear exchange of those lakes with the groundwater system provides a valuable practical reference for solving related conjunctive use problems.
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    The development of a real-time flooding operation model in the Tseng-Wen Reservoir
    (2014)
    Chen, Yu-Wen
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    Tsai, Jul-Pin
    ;
    Chang, Liang-Cheng
    ;
    Ho, Chih-Chao
    ;
    Chen, You-Cheng
    Typhoon events occur frequently in Taiwan resulting in flood-related disasters. A well-operated reservoir can reduce the severity of a disaster. This study incorporates a genetic algorithm, a river hydraulic model, an artificial neural network and a simulation model of Tseng-Wen Reservoir to propose a real-time flooding operation model. The model includes two parts: an optimal flooding operation model (OFOM) and a reservoir inflow forecasting. Given an inflow condition, the OFOM is run based on the safety of the dam structure, reservoir flooding operation rule, and minimization of the downstream loss due to flood. A simple and robust model for reservoir inflow forecasting, which automatically chooses the most similar event from a typhoon event database as the future inflow, is developed. This study compares the model results with the real operations during Typhoons Sepat, Krosa, Kalmaegi, Fung-wong, Sinlaku, and Jangmi. This study compares the performances of the proposed model with the practical operation operated by the management center of Tseng-Wen Reservoir. The proposed model indicates shorter flooding duration in the downstream area. For example, the flood durations of the model output are 4 and 3 hours shorter during Typhoon Krosa and Sinlaku, respectively, than the practical operations.