Including the feature of appropriate adjacent sites improves the PM2.5 concentration prediction with long short-term memory neural network model

被引：0

作者：

Mengfan, Teng ^{[1
]}

Siwei, Li ^{[1
,2
]}

ge, Song ^{[1
]}

jie, Yang ^{[1
,2
]}

Lechao, Dong ^{[1
]}

hao, Lin ^{[1
]}

Senlin, Hu ^{[1
]}

机构：

[1] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan,430079, China

[2] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan,430079, China

来源：

Sustainable Cities and Society | 2022年 / 76卷

关键词：

Engineering Village;

D O I：

103427

中图分类号：

学科分类号：

摘要：

Adjacent sites - Appropriate adjacent site - Concentration prediction - Convolutional neural network - Hybrid model - Long short-term memory neural network - Neural network model - Neural-networks - PM 2.5 - Policy makers

引用

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