基于神经网络技术的水质预测

被引:9
作者
卢金锁
黄廷林
机构
[1] 西安建筑科技大学环境与市政工程学院
关键词
预警系统; 相关分析; 指标聚类; 神经网络; 水质预测;
D O I
暂无
中图分类号
X824 [水质评价];
学科分类号
摘要
随着地表饮用水源的藻类高发程度的加重和藻毒素的对健康的危害性逐渐被认识,供水企业需建立水源水质预警系统以确保供水水质和实现水厂经济运行,而水源水质预测是预警系统的基础.收集天津引滦源水1744天的水质检测资料,通过相关及指标聚类两种方法分析,确定建立预测2天后源水水质-叶绿素-a的神经网络模型的输入变量选择方案共26个,每个方案经10次试验,比选出模型最优输入变量和模型结构.为建立具有代表性的模型,使用前1209天数据训练模型,训练后的模型对剩余数据的仿真输出值与实际值间相关系数达0.88,其预测准确率为85%,满足水厂运行要求.
引用
收藏
页码:43 / 49
页数:7
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