利用先进的机器学习和深度学习技术预测巴格达市的空气质量指数和细颗粒物水平

IF 1.8 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Anees A. Khadom , Saad Albawi , Ali J. Abboud , Hameed B. Mahood , Qusay Hassan
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引用次数: 0

摘要

颗粒物污染是全球公认的最有害的空气污染形式之一,对环境完整性和公众健康造成了深远影响。评估这种污染的关键指标包括空气质量指数(AQI)和直径≤2.5 μm 的细颗粒物(PM2.5)。这些指标与严重的健康后果密切相关,如长期接触会导致过早死亡。虽然一些研究采用了传统的统计方法来评估空气质量指数和 PM2.5,但先进的机器学习技术的应用还很有限。本研究采用深度学习和人工神经网络(ANN)来预测伊拉克巴格达的空气质量指数和 PM2.5 水平。研究利用了从 2016 年 7 月 1 日至 2021 年 12 月 12 日的大量数据集,其中包括 48,000 多个空气质量指数和 PM2.5 的数据点。时间是影响这些因变量的独立输入变量。分析采用了多种机器学习算法,包括随机森林、决策树、K-近邻(KNN)、多层感知器(MLP)和长短期记忆网络(LSTM)。研究结果表明,MLP 和 LSTM 模型优于其他方法,能提供最准确的预测。使用 MLP 和 LSTM 预测空气质量指数的相关系数分别为 0.977 和 0.983,预测 PM2.5 的相关系数分别为 0.973 和 0.985。此外,研究结果表明,空气质量指数(AQI)和 PM2.5 均在中等至不健康范围内,其分布水平表明需要解决巴格达市的空气质量问题。此外,本研究还建立了一个用于评估空气质量的稳健而细致的预测框架,为环境科学中机器学习应用的蓬勃发展做出了贡献。它凸显了深度学习在公共卫生应用中的潜力,并为减轻空气污染及其不利影响的决策提供了可行的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting air quality index and fine particulate matter levels in Bagdad city using advanced machine learning and deep learning techniques

Particulate matter pollution is recognized globally as one of the most hazardous forms of air pollution, profoundly impacting environmental integrity and public health. Key metrics for assessing this pollution include the Air Quality Index (AQI) and fine particulate matter with diameters ≤2.5 μm (PM2.5). These indicators are closely associated with severe health consequences, such as premature death from chronic exposure. While traditional statistical methods have been employed in some studies to evaluate AQI and PM2.5, the application of advanced machine learning techniques has been limited. This research employs deep learning and artificial neural networks (ANN) to forecast AQI and PM2.5 levels in Baghdad, Iraq. The study utilizes an extensive dataset from July 1, 2016, to December 12, 2021, comprising over 48,000 data points for AQI and PM2.5. Time serves as an independent input variable influencing these dependent variables. The analysis employs a diverse set of machine learning algorithms, including random forest, decision tree, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory networks (LSTM). The findings demonstrate that MLP and LSTM models outperform other methods, providing the most accurate predictions. The correlation coefficients were 0.977 and 0.983 for the prediction of AQI and 0.973 and 0.985 for the prediction of PM2.5 using MLP and LSTM, respectively. In addition, the outcomes showed that both AQI and PM2.5 were within the moderate to unhealthy ranges, and their distribution levels pointed to the need for addressing air quality in Baghdad city. Furthermore, this study contributes to the burgeoning field of machine learning applications in environmental science by establishing a robust and nuanced predictive framework for evaluating air quality. It highlights the potential of deep learning in public health applications and offers actionable insights for policymaking to mitigate air pollution and its adverse effects.

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来源期刊
Journal of Atmospheric and Solar-Terrestrial Physics
Journal of Atmospheric and Solar-Terrestrial Physics 地学-地球化学与地球物理
CiteScore
4.10
自引率
5.30%
发文量
95
审稿时长
6 months
期刊介绍: The Journal of Atmospheric and Solar-Terrestrial Physics (JASTP) is an international journal concerned with the inter-disciplinary science of the Earth''s atmospheric and space environment, especially the highly varied and highly variable physical phenomena that occur in this natural laboratory and the processes that couple them. The journal covers the physical processes operating in the troposphere, stratosphere, mesosphere, thermosphere, ionosphere, magnetosphere, the Sun, interplanetary medium, and heliosphere. Phenomena occurring in other "spheres", solar influences on climate, and supporting laboratory measurements are also considered. The journal deals especially with the coupling between the different regions. Solar flares, coronal mass ejections, and other energetic events on the Sun create interesting and important perturbations in the near-Earth space environment. The physics of such "space weather" is central to the Journal of Atmospheric and Solar-Terrestrial Physics and the journal welcomes papers that lead in the direction of a predictive understanding of the coupled system. Regarding the upper atmosphere, the subjects of aeronomy, geomagnetism and geoelectricity, auroral phenomena, radio wave propagation, and plasma instabilities, are examples within the broad field of solar-terrestrial physics which emphasise the energy exchange between the solar wind, the magnetospheric and ionospheric plasmas, and the neutral gas. In the lower atmosphere, topics covered range from mesoscale to global scale dynamics, to atmospheric electricity, lightning and its effects, and to anthropogenic changes.
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