基于神经网络的城内湖水华预警综合建模方法研究.PDF

中国环境科学 2017,37(5) ：1872~1878 China Environmental Science 基于神经网络的城市内湖水华预警综合建模方法研究 郑剑锋 1,2*,焦继东 1,孙力平 1,2 (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) 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 pH 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 70.98µg/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. 关键词：水华；风险概率；预警等级；预警因子；预警模型 中图分类号：X524 文献标识码：A 文章编号：1000-6923(2017)05-1872-07 A modeling approach for early-warning of water bloom risk in urban lake based on neural network. ZHENG Jian-feng1,2* 1 1,2 , JIAO Ji-dong , SUN Li-ping (1.School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China ；2.Tianjin Key Laboratory of Aquatic Science and Technology, Tianjin 300384, China). China Environmental Science, 2017,37(5)：1872~1878 Abstract ：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 thatthe threshold value of