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Volume 40 Issue 6
Sep.  2022
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Article Navigation >  ENVIRONMENTAL ENGINEERING  > 2022  >  40(6): 226-232,153
XIA Rui, JIA Ruining, CHEN Yan, WANG Lu, MA Shuqin, ZHANG Yuan. PROSPECTS OF SIMULATION METHODS FOR WATERSHED AQUATIC ECOSYSTEM INTEGRITY[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 241-252. doi: 10.13205/j.hjgc.202206031
Citation: LI Junyu, LIU Shuming, WU Xue, XIE Tao, JIN Ye. OPTIMIZATION OF URBAN WATER SUPPLY NETWORK BASED ON DYNAMIC PRUNING MODEL[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 226-232,153. doi: 10.13205/j.hjgc.202206029
shu

OPTIMIZATION OF URBAN WATER SUPPLY NETWORK BASED ON DYNAMIC PRUNING MODEL

doi: 10.13205/j.hjgc.202206029

  • 1. School of Environment, Tsinghua University, Beijing 100084, China;

  • 2. Beijing Waterworks Group Shijingshan District Co., Ltd, Beijing 100043, China;

  • 3. Beijing Waterworks Group Co., Ltd, Beijing 100031, China

  • Received Date: 2022-02-07
    Available Online: 2022-09-01
  • Publish Date: 2022-09-01
    Abstract
  • The optimal scheduling model for urban water supply networks often requires repeated scheduling of the hydraulics model to calculate the objective function and constraints,which leads to high computational and time cost.In the optimization iteration process,searching is usually performed at the boundaries of the constraints in the search space to obtain better optimization results,which leads to a large number of samples that do not satisfy the constraints and optimization efficiency.To solve this problem,an efficient constraint-based dynamic pruning method was proposed,which used the data accumulated during the optimization calculation to determine whether the samples met the constraints before running the hydraulics model,to eliminate the samples that do not satisfy the constraints and improve the optimization efficiency.The model was tested on a case network and a real network,and the results showed that the use of Naïve Bayes,decision trees,and support vector machines as dynamic pruning models reduced the number of computation by 56.4%,58.5%,and 56.8%,respectively,while obtaining the similar results as the original optimization model.
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    • References(17)
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