Browsing by Author "Pan, Chen-Che"

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Determining an Optimal Action Portfolio for Water Resource Management by Using Stochastic Programming
(2017-07-01)
Ho, Chih-Chao
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Pan, Chen-Che
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Chang, Liang-Cheng
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土木工程學系
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Department of Civil Engineering
A regional water management system always contains long-term and short-term actions in practice. The uncertainty of short-term actions increases great complexity for evaluating the performance of action portfolio comprised with long-term and short-term actions. Developing an efficient methodology to define a cost-effective action portfolio is an important task. Hence, this study develops a novel decision model, the Stochastic Programming with Recourse Decision Model (SPRDM), to compute a cost-effective action portfolio. The effectiveness of SPRDM is verified by address a problem of water shortage and financial cost in Taoyuan City, Northern Taiwan. The results shown adding Kaotai Reservoir alone can fulfill the Taoyuan demand most of the time, and the remaining extreme water shortage events can be addressed by short-term and irrigation management actions additionally. When drought duration of water shortage event is longer than 200 days, using irrigation management action to address water deficit has lower expected costs than other short-term actions do. Although the study focuses on Northern Taiwan, the proposed model is applicable to other areas with an integrated water resources management framework.
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
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Chen, Yu-Wen
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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.
High Recharge Areas in the Choushui River Alluvial Fan (Taiwan) Assessed from Recharge Potential Analysis and Average Storage Variation Indexes
(2015-04-01)
Tsai, Jui-Pin
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Chen, Yu-Wen
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Chang, Liang-Cheng
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Kuo, Yi-Ming
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Tu, Yu-Hsuan
High recharge areas significantly influence the groundwater quality and quantity in regional groundwater systems. Many studies have applied recharge potential analysis (RPA) to estimate groundwater recharge potential (GRP) and have delineated high recharge areas based on the estimated GRP. However, most of these studies define the RPA parameters with supposition, and this represents a major source of uncertainty for applying RPA. To objectively define the RPA parameter values without supposition, this study proposes a systematic method based on the theory of parameter identification. A surrogate variable, namely the average storage variation (ASV) index, is developed to calibrate the RPA parameters, because of the lack of direct GRP observations. The study results show that the correlations between the ASV indexes and computed GRP values improved from 0.67 before calibration to 0.85 after calibration, thus indicating that the calibrated RPA parameters represent the recharge characteristics of the study area well; these data also highlight how defining the RPA parameters with ASV indexes can help to improve the accuracy. The calibrated RPA parameters were used to estimate the GRP distribution of the study area, and the GRP values were graded into five levels. High and excellent level areas are defined as high recharge areas, which composed 7.92% of the study area. Overall, this study demonstrates that the developed approach can objectively define the RPA parameters and high recharge areas of the Choushui River alluvial fan, and the results should serve as valuable references for the Taiwanese government in their efforts to conserve the groundwater quality and quantity of the study area.
The Decision Model for Integrated Water Resources Management
(2013)
Ho, Chih-Chao
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Chang, Liang-Cheng
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Pan, Chen-Che
;
Tsai, Chan-Ming
;
Tu, Yi-Te
The proposed strategies of Taiwan water resources planning are always used for solving long-term water supply problem. However extreme events have happened frequently under global climate change. The water supply suffers a severe challenge. The short-term shortage can\'t be ignored. Water utilities can take numerous actions to improve water availability or cope with shortages. Such as long-term investments to expand infrastructure, and short-term measures to flexibly transfer, ration, or curtail some uses, represent several different approaches, timings, and spatial scales for management. That makes the decision more complex and difficult. Hence, the study proposes a Stochastic Programming with Recourse Decision Model (SPRDM) to solving this puzzle. SPRDM is applied to decide the best strategies combination of Northern Taiwan under future demand (2021). The study identifies the best solutions to reach sustainable purpose for water resources management.