摘要
为科学评估河口型水源地供水安全风险,基于水源地特点,在建立相应供水安全风险评估指标体系的基础上,采用层次分析法确定指标权重,结合选取灰色关联度法和BP神经网络法对某河口型水源地分别开展供水安全风险分类评估。以某次咸潮入侵事件数据为例,经采用两种方法评估后对比表明:灰色关联度法能较好地解决评价指标难以准确量化和统计的问题,具有定性与定量相结合评价精度较高的优点,更适合于河口型水源地供水安全风险分类评估。该水源地风险分类评价等级由高到低依次排序:水量风险(较高)→应急风险(较低)→工程风险(较低)→水质风险(低),其结果与实际相符。
Concerning the characteristics of the water source,the risk assessment of safety water supply from estuary water source is based on risk assessment index system and confirmed-indicator-weights(based on AHP method).To scientifically evaluate the security of water supply from the estuary water source,we propose grey correlation and BP neural network for risk classification assessment of a water source.We take the data of the salt-water intrusion event as an example.Comparing the two methods,we come to the conclusion that the gray correlation method can better solve the problem accurately in a more quantified and statistical way.There is also the advantage of higher accuracy when combining qualitative and quantitative evaluations.Therefore gray correlation is more suitable for risk classification assessment of water supply security in the estuary water source.The risk classification assessment levels of the water source are ranked in a descending order that water risk(higher)is higher than emergency risk(lower).Emergency risk(lower)is higher than engineering risk(lower).Engineering risk(lower)is higher than water quality risk(low).The result is consistent with the actual project.
作者
陈缘
孔令婷
刘曙光
徐贵泉
CHEN Yuan;KONG Lingting;LIU Shuguang;XU Guiquan(Department of Hydraulic Engineering, Tongji University, Shanghai 200092, China;Shanghai Water Planning and Design Institute, Shanghai 200233, China)
出处
《海洋工程》
CSCD
北大核心
2020年第4期118-128,共11页
The Ocean Engineering
基金
国家重点研发计划项目(2017YFC0405400)。
关键词
水源地
供水安全
风险评估
灰色关联度
神经网络
estuary water source
water supply safety
risk assessment
grey correlation
neural networks