Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic,WIREs Data Mining and Knowledge Discovery

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Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2021-06-30 , DOI: 10.1002/widm.1423
Krittakom Srijiranon ₁ , Narissara Eiamkanitchat ₁ , Sakgasit Ramingwong ₁ , Kenneth Cosh ₁ , Lachana Ramingwong ₁

Affiliation

Coarse particulate matter (PM₁₀), the inhalable particles with an aerodynamic diameter smaller than 10 micrometers are one of the major air pollutions that affect human health. Over the previous decade, a number of researchers applied various data mining techniques to create a temporal prediction model. This study reviews and discusses 100 research articles in computer science and environmental science coming from the Scopus database. The three processes of data mining techniques, including data preparation, model creation, and model evaluation for prediction PM₁₀ are highlighted. A summary of the overall process directions of data mining as well as their output are revealed. Additionally, recommendations for future research are identified.

中文翻译：

利用数据挖掘技术进行 PM10 预测的调查：按主题分析

粗颗粒物（PM ₁₀），空气动力学直径小于10微米的可吸入颗粒，是影响人类健康的主要空气污染之一。在过去的十年中，许多研究人员应用各种数据挖掘技术来创建时间预测模型。本研究回顾并讨论了来自 Scopus 数据库的 100 篇计算机科学和环境科学研究文章。重点介绍了数据挖掘技术的三个过程，包括数据准备、模型创建和预测 PM _{10 的}模型评估。揭示了数据挖掘的总体过程方向及其输出的摘要。此外，还确定了对未来研究的建议。

更新日期：2021-08-12

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