基于Storm的AQI实时预测模型

论文摘要

为解决大数据条件下,预测空气质量指数AQI存在计算耗时长,不能进行实时预测等问题,提出一种基于Storm的AQI实时预测模型S-OKNN。使用Storm计算框架,对KNN算法进行分布式拓展;利用Storm实时流数据计算特点,使模型能够实时预测AQI。实验结果表明,在保证预测精度的前提下,基于Storm的AQI实时预测模型S-OKNN比普通的KNN算法具有更快的处理速度、更高的吞吐量、更能满足实时预测AQI的需求。

论文目录

文章来源

类型: 期刊论文

作者: 刘洪通,冯百明,温向慧,车翔飞

关键词: 框架,最近邻算法,空气质量指数,实时预测,大数据

来源: 计算机工程与设计 2019年01期

年度: 2019

分类: 信息科技,工程科技Ⅰ辑

专业: 环境科学与资源利用,计算机软件及计算机应用

单位: 西北师范大学计算机科学与工程学院

基金: 国家自然科学基金项目(61662067,61462076)

分类号: X51;TP311.13

DOI: 10.16208/j.issn1000-7024.2019.01.050

页码: 296-301

总页数: 6

文件大小: 234K

下载量: 218

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标签：框架论文;  最近邻算法论文;  空气质量指数论文;  实时预测论文;  大数据论文;