A hydraulic model-based emergency schedul- ing Decision Support System （DSS） is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources...A hydraulic model-based emergency schedul- ing Decision Support System （DSS） is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources. The DSS consists of four functional modules, including water quality prediction, system safety assessment, emergency strategy inference and scheduling optimization. The work flow of the DSS is as follows. First, the water quality variations on specific cross-sections are calculated given the pollution information. Next, a comprehensive evaluation on the safety of the current system is conducted using the outputs in the first module. This will assist in the assessment of whether the system is in danger of failure, taking both the impact of pollution and system capacity into account. If there is a severe impact of contamination on the reliability of the system, a fuzzy logic based inference module is employed to generate reason- able strategies including technical measures. Otherwise, a Genetic Algorithm （GA）-based optimization model will be used to find the least-cost scheduling plan. The proposed DSS has been applied to a coastal city in South China during a saline tide period as validation. Through scenario analysis, it is demonstrated that this DSS tool is instrumental in emergency scheduling for the water company to quickly and effectively respond to sudden contamination incidents.展开更多
基金funded by the National Natural Science Foundation of China(51922096)the Excellent Youth Natural Science Foundation of Zhejiang Province,China(LR19E080003)the support from the Hong Kong Research Grants Council(RGC)(15200719)。
文摘A hydraulic model-based emergency schedul- ing Decision Support System （DSS） is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources. The DSS consists of four functional modules, including water quality prediction, system safety assessment, emergency strategy inference and scheduling optimization. The work flow of the DSS is as follows. First, the water quality variations on specific cross-sections are calculated given the pollution information. Next, a comprehensive evaluation on the safety of the current system is conducted using the outputs in the first module. This will assist in the assessment of whether the system is in danger of failure, taking both the impact of pollution and system capacity into account. If there is a severe impact of contamination on the reliability of the system, a fuzzy logic based inference module is employed to generate reason- able strategies including technical measures. Otherwise, a Genetic Algorithm （GA）-based optimization model will be used to find the least-cost scheduling plan. The proposed DSS has been applied to a coastal city in South China during a saline tide period as validation. Through scenario analysis, it is demonstrated that this DSS tool is instrumental in emergency scheduling for the water company to quickly and effectively respond to sudden contamination incidents.
