| Summary: | 博士 === 國立交通大學 === 土木工程系所 === 97 === The uncertainty of simulation modeling is resulted from the simplified assumptions of reality and uncertainty of parameter estimation. With more information regarding aquifer characterization, the devised pumping strategy will be more cost-effective. The pumping and the monitoring strategies must be designed simultaneously, so that the trade-off between the cost of monitoring strategy and the value of information, i.e. pumping cost saving, could be appraised. For the flexibility of adjusting the well networks after updating the groundwater system, the well networks should be with capacity that expands with time.
In this study, a real-time management model for jointly optimizing the pumping and monitoring stratgies of groundwater supply problem considering the system capacity expansion is presented. The proposed approcah hybridizes a Genetic algorithm (GA) and Simulation annealing (SA). The chromosomes of the GA represent a possible design alternative, a pumping and monitoring network with capacity that expands with time. The SA is then used to compute the optimal pumping policy associated with the chromosome. The information obtained through monitoring network is used to update the hydraulic head and which covariance matrix with Kalman filtering. The hybrid algorithm can be effectively parrallelized by the implicit parallel characteristic of GA. This study implements the parallel computation to overcome the increasing computational requirements due to the complexity of the problem as well as using GA and SA. A hypothetical confined aquifer is included to demonstrate the proposed apparoch and to illustrate the interaction between pumping and monitoring strategies. Results show that the ratio of monitoring cost to total cost is relatively small. Through integrating the pumping and monitoring designs and redesign the well networks with time, the monitoring strategy could significantly reduce the pumping cost.
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