摘要
以宁波大学校内池塘2009年3—10月间30周的监测数据为基础,运用BP人工神经网络方法构建预测模型,探求颤藻生物量与总氮、总磷、透明度等6项环境因子之间的关系,选出最佳预测模型,并对模型进行敏感度分析。结果显示:①BP神经网络模型对颤藻生物量预测值与实测值之间拟合程度良好,相关系数达到了0.984,说明BP神经网络模型可以用于水体中藻类水华的短期预测。②通过对构建的BP神经网络模型进行敏感度分析,阐明了宁波大学校内池塘藻类水华的主要驱动因素,并指出控制水体的pH是宁波大学校内池塘藻类水华防治工作的重点。
According to the 30 weeks of monitored data from March to October in 2009 in the School pond of Ningbo University,we constructed a predicting model to deal with the relation between the density of Oscillatoria and 6 environmental factors such as total nitrogen,total phosphorus,secchi depth,etc with the back propagation artifical neutral network method.We selected the best predicting model,and sensitivity analysis was performed to the model.The results showed that the forecasted value of the density of Oscillatoria according to the BP neural network predicting model had a better fit with actual value of the density of Oscillatoria,and the correlation coefficient achieved 0.984,it indicated the BP neural network predicting model can be used for short-term forecast of the algal blooms;and through carried on sensitivity analysis to the constructed BP neural network predicting model,it clarified the main driver factor of algal blooms in the School pond of Ningbo University,and the result showed that controlling PH value would be important to prevent and control the algal blooms in the School pond of Ningbo University.
出处
《中国环境监测》
CAS
CSCD
北大核心
2012年第3期53-57,共5页
Environmental Monitoring in China
基金
国家自然科学基金资助项目(30771658)
浙江省自然科学基金资助项目(Z505319)
宁波市科技计划项目(2008C50017)
关键词
BP神经网络
藻华水体
预测模型
颤藻生物量
BP neural network
Algal bloom
Predicting model
The density of Oscillatoria
