基于神经网络的城市内湖水华预警综合建模方法研究  

A modeling approach for early-warning of water bloom risk in urban lake based on neural network

作　　者：郑剑锋[1,2] 焦继东 孙力平[1,2] ZHENG Jian-feng;JIAO Ji-dong;SUN Li-ping(School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China;Tianjin Key Laboratory of Aquatic Science and Technology, Tianjin 300384, China)

机构地区：[1]天津城建大学环境与市政工程学院,天津300384 [2]天津市水质科学与技术重点实验室,天津300384

出　　处：《中国环境科学》2017年第5期1872-1878,共7页China Environmental Science

基　　金：天津市自然科学基金(15JCYBJC49100);天津水质科学与技术重点实验室开放基金(TJKLAST-ZD-2015-01)

摘　　要：针对城市内湖水华产生过程存在复杂性、时变性、不确定性等特点,运用内集-外集、粗糙集约简和RBF神经网络模型,通过水华藻生物量阈值界定、风险概率计算、预警等级划分、预警因子识别和神经网络预测模型的研究,提出一种城市内湖水华预警综合建模方法.以天津清净湖为例,利用p H值、水温等12项水质指标监测数据,确定清净湖水华的叶绿素a浓度阈值为70.98?g/L,依据水华风险概率划分5个水华预警等级,并确定水温、溶解氧、高锰酸盐指数和TDS为水华预警因子.利用RBF神经网络技术构建清净湖水华预警模型,验证结果显示,模型预测精度达85.7%,表明该方法能较好地用于城市内湖水华预警模型构建.Formation process of water bloom was complicated,time-varied and uncertain.So far water bloom prediction of urban lake was still difficult.An integrated modeling approachby using interior-outer-set,rough sets reduction algorithm and RBF neural network model was proposed for early-warning of water bloom risk.Interior-outer-set model was employed to define the threshold of chlorophyll a for predictingwater bloom risk,and a method was put forward for calculating the risk probability of water bloom.Rough sets reduction algorithm was used to identify the keydriving factors ofwater bloom.An early-warning model of water bloom risk was developed based on RBF neural network model.Feasibility of themodeling approach was proved though the application in Qingjing Lake.The results indicated that the threshold value of chlorophyll a was70.98μg/L;water bloom risk was divided into five grades based on the riskm probability of water bloom;fourwater quality indexes including water temperature,dissolved oxygen,permanganate index and total dissolved solids were identified as the indicators of water bloom.Result of model validation showed that the RBF neural network model's accurate rate exceeded85%,and could be applied to early-warning of water bloom risk in Qingjing Lake.

关 键 词：水华 风险概率 预警等级 预警因子 预警模型 

分 类 号：X524[环境科学与工程—环境工程]