Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI:10.1002/widm.1423

Krittakom Srijiranon, Narissara Eiamkanitchat, Sakgasit Ramingwong, K. Cosh, L. Ramingwong

引用次数: 1

Abstract

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.

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利用数据挖掘技术预测PM10的研究:按主题分析

粗颗粒物(PM10)是空气动力学直径小于10微米的可吸入颗粒物，是影响人类健康的主要空气污染物之一。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。本研究对来自Scopus数据库的100篇计算机科学和环境科学研究论文进行了综述和讨论。重点介绍了用于PM10预测的数据挖掘技术的三个过程，包括数据准备、模型创建和模型评估。总结了数据挖掘的总体过程方向及其输出。此外，对未来的研究提出了建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.