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水力发电学报 ›› 2023, Vol. 42 ›› Issue (10): 27-39.doi: 10.11660/slfdxb.20231003

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基于传染病动力模型的供水风险传递规律研究

  

  • 出版日期:2023-10-25 发布日期:2023-10-25

Study on water supply risk transfer laws based on infectious disease dynamic model

  • Online:2023-10-25 Published:2023-10-25

摘要: 针对跨流域调水工程供水风险传递规律不明确、风险动态管理效果不足等问题,以引汉济渭工程初期为例,识别风险因子并采用专家打分与突变系数法量化风险,基于传染病动力学创建供水风险传递模型,设置6种风险方案,揭示引汉济渭初期供水风险的传递规律。结果表明:产生供水风险的月份与风险源数量呈正相关;不考虑径流预报误差风险时,越早采取优化调度措施,出现供水风险的月份越少、恢复性越高;考虑径流预测误差时,采取优化调度措施对供水风险的控制效果不显著;风险的传递能力与风险数量不存在正相关,风险的破环性与风险源的数量呈正相关。研究成果对调水工程运行中供水风险防控、制定风险减控方案有重要意义。

关键词: 传染病动力学模型, 供水风险, 传递规律, 反向传播神经网络, 跨流域调水工程

Abstract: Inter-basin water transfer projects suffer from the issues of unclear water supply risk transfer laws and unsatisfactory dynamic risk management effect. To explore the risk transfer law, this paper identifies risk factors, quantifies the risks using the expert scoring and mutation coefficient method, and develops a water supply risk transfer model based on the infectious disease dynamics. We have applied this model in a case study of the initial stage of the Hanjiang-to-Weihe River Water Diversion Project, and examined six risk schemes to reveal its risk transfer law. The results show the number of months under water supply risks is positively correlated with the number of risk sources. When the runoff forecast error risk is not considered, an earlier implementation of the optimal operation measures leads to a shorter water supply risk period and a higher recovery level; When the error risk is considered, the control effect of optimal operation measures on water supply risks is insignificant. No positive correlation is observed between the risk transfer capability and the number of risks; risk damage is positively correlated with the number of risk sources. The results are of great significance for prevention and control of water supply risks and risk reduction and control programs of a water diversion project.

Key words: infectious disease dynamic model, water supply risk, transmission law, back-propagation neural network, inter-basin water transfer project

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