基于随机森林模型的大气污染物PM2.5预测研究

IF 1.3 Q4 ENVIRONMENTAL SCIENCES Environmental Health Engineering and Management Journal Pub Date : 2023-05-25 DOI:10.34172/ehem.2023.18
S. Babu, B. Thomas
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引用次数: 1

摘要

背景:造成空气污染的最关键因素之一是颗粒物(PM2.5),其急性或慢性暴露会对人类健康造成严重影响。准确预测PM2.5浓度对于控制空气污染和预防健康并发症至关重要。本文对用于PM2.5预测的随机森林模型的现有科学文献进行了调查。方法:根据一组指定的搜索标准,从Science Direct数据库中提取科学文献。本研究分析了PM2.5预测中使用的输入特征、数据长度和评估参数。结果:研究表明,大多数出版物都是针对户外PM2.5的日常预测。大多数出版物的PM2.5预测是基于气溶胶光学深度(AOD)和边界层高度(BLH)的特征。PM10和NO2是PM2.5估算中使用的主要空气污染物。大多数研究使用的输入数据长度超过一年,预测模型的有效性不受调查长度的影响。决定系数R2是所有出版物中使用的主要评估参数。大多数研究表明R2值大于0.85,证明了基于随机森林回归的PM2.5预测模型的合理可靠性和有效性。结论:研究表明,根据研究背景和数据可及性,出版物使用了各种气象和地质特征来估计PM2.5。研究结果表明,很难以任何特定的方式精确定位最优模型。
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A survey on air pollutant PM2.5 prediction using random forest model
Background: One of the most critical contributors to air pollution is particulate matter (PM2.5) that its acute or chronic exposure causes serious health effects to human. Accurate forecasting of PM2.5 concentration is essential for air pollution control and prevention of health complications. A survey of the available scientific literature on random forest model for PM2.5 prediction is presented here. Methods: The scientific literature is extracted from Science Direct database based on a set of specified search criteria. The input features, data length, and evaluation parameters used in PM2.5 prediction were analyzed in this study. Results: The study shows that majority of the publications are aimed at the daily prediction of outdoor PM2.5. Most publications base their PM2.5 prediction on features aerosol optical depth (AOD) and boundary layer height (BLH). PM10 and NO2 are the main air pollutants employed in the PM2.5 estimation. Majority studies utilized input data lengths covering more than one year, and the effectiveness of prediction models are unaffected by the length of investigation. The coefficient of determination, R2 , is the primary evaluation parameter used in all publications. The majority of research study indicated R2 values greater than 0.85, demonstrating the reasonable dependability and efficiency of random forest regression-based PM2.5 prediction models. Conclusion: The study demonstrates that the publications use a variety of meteorological and geological features for PM2.5 estimation, depending on the context of the research as well as data accessibility. The findings demonstrate that it is hard to pinpoint the optimal model in any particular way.
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来源期刊
CiteScore
2.40
自引率
37.50%
发文量
17
审稿时长
12 weeks
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