论文部分内容阅读
利用地理信息系统(GIS)对水厂原水水质信息进行管理,将水质信息与水厂的空间信息有机联系,可提高水厂水质管理水平。采用径向基函数(RBF)神经网络对杭州市的4座水厂原水中的藻类含量进行预测,建立藻类含量预测模型,结果表明,RBF神经网络藻类含量预测模型与常用的细胞计数法相结合,可提高藻类含量预测精度,同时采用GIS技术将藻类含量预测结果以空间图形形式显示和输出,更具可视性,可为水厂有效地控藻除藻提供支持。
The use of Geographic Information System (GIS) to manage the raw water quality information of waterworks and the organic connection between water quality information and spatial information of waterworks can improve the water quality management of waterworks. The radial basis function (RBF) neural network was used to predict the algae content in the raw water of the four waterworks in Hangzhou, and the algae content prediction model was established. The results showed that the prediction model of algal content in RBF neural network combined with the commonly used cell counting method, Which can improve the prediction accuracy of algae content. At the same time, GIS technology is used to display and output the forecast result of algae content in the form of spatial graph, which can provide more support for the water plant to effectively control algae algae removal.