| 城市湖泊水华预警模型研究——以北京“六海”为例 |
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| 引用本文： | 曾勇,杨志峰,刘静玲.城市湖泊水华预警模型研究——以北京“六海”为例[J].水科学进展,2007,18(1):79-85. |
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| 作者姓名： | 曾勇 杨志峰 刘静玲 |
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| 作者单位： | 水环境模拟国家重点实验室, 北京师范大学环境学院, 北京, 100875 |
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| 基金项目： | 国家自然科学基金;国家重点基础研究发展计划(973计划) |
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| 摘 要： | 采用决策树方法和非线性回归方法建立湖泊水华预警模型。决策树方法预测水华爆发时机,非线性回归方法预测水华爆发强度,并运用信号灯显示方法,划分出水华爆发的预警区间。以北京六海为例,模型结果表明来水水量Q,温度T和总磷浓度是影响“六海”湖泊水华爆发的主要影响因子,选择叶绿素a(Chl-a)<30 μg/L的预警信号为绿色,30 μg/L60 μg/L为红色。当每月来水量Q>79.0万m3或来水量Q<79.0万m3,水温<13.4℃,预警指标为绿色;Q<79.0万m3,水温T>13.4℃,水华预警为黄色;Q<38.7万m3时,T>23.25℃,TP>0.13 mg/L,水华预警为红色。对模型结果分类进行了验证。结果表明:模型对于限制因素发生变化时的水华预测结果更为准确,并且结构简单,输入输出关系明显,结果易于解释。
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| 关 键 词： | 水华 预警 决策树 非线性回归 信号灯显示 北京六海 |
| 文章编号： | 1001-6791（2007）01-0079-07 |
| 收稿时间： | 2005-09-16 |
| 修稿时间： | 2006-02-24 |
Algalbloom prediction models for Liuhai-lake in Beijing city |
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| ZENG Yong,YANG Zhi-feng,LIU Jing-ling.Algalbloom prediction models for Liuhai-lake in Beijing city[J].Advances in Water Science,2007,18(1):79-85. |
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| Authors: | ZENG Yong YANG Zhi-feng LIU Jing-ling |
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| Affiliation: | State Key Laboratory of Water Environment Simulation, School of Environmental, Beijing Normal University, Beijing 100875, China |
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| Abstract: | The algalbloom prediction models are constructed by using the decision trees to qualitatively predict bloom timing and use the nonlinear piecewise regression to quantitatively predict bloom intensity.The traffic light systems are used as the indicator for degree of algal bloom.Liuhai-lake in Beijing city is used an example.The constructed model indicates that the water inflow,the temperature and total phosphorus are the most impact factors on the algalbloom in Liuhai-lake.The concentration of Chl-a<30 μg/L is labeled as green,30 μg/L60 μg/L as red.When water inflow Q>79.0×104m3 or Q<79.0×104m3 and water temperature<13.4℃,the indicator is green;when Q<79.0×104m3, water temperature T>13.4℃,the indicator is yellow;and when Q<38.7×104m3,water temperature>23.25℃,TP>0.13 μg/L,the indicator is red.The model is test by an independent dataset from the same area,the predicted blooming time error rate and the error of predicted bloom intensity are presented in the paper.The model has great advantages to deal with the common problem in algal-blooms.It's more accurate when the limiting factor is changing.And the structure is understandable and easy to interpret. |
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| Keywords: | algalbloom prediction decision trees nonlinear regression traffic light system Liuhai-lake in Beijing city |
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