计算机科学
聚类分析
空气质量指数
循环神经网络
人工神经网络
索引(排版)
数据挖掘
人工智能
气象学
地理
万维网
作者
Jia Zhu,Baofeng Li,Hong Chen
出处
期刊:Journal of physics
[IOP Publishing]
日期:2021-08-01
卷期号:2006 (1): 012022-012022
标识
DOI:10.1088/1742-6596/2006/1/012022
摘要
Abstract The short term air quality index can usually be predicted by statistical and numerical methods, but for the multi-point prediction of AQI, the traditional methods are often inaccurate. In this paper, a new hybrid multi-point prediction method was proposed by combining K-means clustering with the circulating neural network long and short time memory (RNN-LSTM) model. Based on this prediction method, the air quality index in Dezhou was predicted 1-5 days in advance by using 28 multi-point pollution monitoring sensor data from January 1, 2018 solstice to August 31. The prediction results show that the model not only improves the accuracy and effectiveness of the prediction, but also reveals the relationship between land use patterns and air quality index (AQI), which provides important information for land use planning, air pollution mitigation and urban intelligent governance.
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