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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 9
2640-2645

Air quality prediction model based on spatial-temporal similarity LSTM

Fang Wei
Zhu Runsu
Jiangsu Provincial Engineering Laboratory of Pattern Recognition & Computational Intelligence, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2021.01.0011

Abstract

People have widely used air quality prediction models composed of traditional machine learning methods. However, such models still have shortage on data validity, especially the validity of spatial-temporal related data selection. In response to the input data validity of deep learning networks, this paper proposed a prediction model based on spatial-temporal similarity LSTM to select more effective data at the time and space level. STS-LSTM contained three modules, pre-order, middle-order and post-order module. The pre-order module was the time-space similar selection input module, which proposed the GCWDTW algorithm. This paper used it to select data with higher temporal and spatial similarity. The in-order module used LSTM as a deep learning network for training. The post-order module selected different output groups for integration according to the characteristics of the target station. The STS-LSTM overall model improves the air quality prediction error by about 8% compared with the existing algorithm, and the data selected by the validity improves the accuracy of the model by up to 21%. The experimental results show that this model has achieved significant results in the effective data selection, and the data input and output method as a part of the applied deep learning network can effectively improve the final effect of the deep learning network.

Foundation Support

国家重点研发计划资助项目(2017YFC1601800,2017YFC1601000)
国家自然科学基金资助项目(62073155,61673194,61672263)
江苏省重点研发计划资助项目(BE2017630)
江苏高校“青蓝”工程资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.01.0011
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 9
Section: Algorithm Research & Explore
Pages: 2640-2645
Serial Number: 1001-3695(2021)09-014-2640-06

Publish History

[2021-09-05] Printed Article

Cite This Article

方伟, 朱润苏. 基于时空相似LSTM的空气质量预测模型 [J]. 计算机应用研究, 2021, 38 (9): 2640-2645. (Fang Wei, Zhu Runsu. Air quality prediction model based on spatial-temporal similarity LSTM [J]. Application Research of Computers, 2021, 38 (9): 2640-2645. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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