Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling

被引:4
|
作者
Liu, Qian [1 ]
Cui, Bingyan [2 ]
Liu, Zhen [3 ]
机构
[1] Zhejiang Univ Water Resources & Elect Power, Coll Elect Engn, Hangzhou 310018, Peoples R China
[2] Rutgers State Univ, Dept Civil & Environm Engn, Piscataway 08854, NJ USA
[3] Penn State Univ, Thomas D Larson Penn Transportat Inst, University Pk, PA 16802 USA
关键词
air quality; machine learning; statistical analysis; secondary modeling; prediction model;
D O I
10.3390/atmos15050553
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Machine Learning Methods for Air Quality Monitoring
    Zaytar, Mohamed Akram
    El Amrani, Chaker
    [J]. 3RD INTERNATIONAL CONFERENCE ON NETWORKING, INFORMATION SYSTEM & SECURITY (NISS'20), 2020,
  • [2] Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques
    Devasekhar, V.
    Natarajan, P.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (04) : 927 - 937
  • [3] Air Quality Prediction Of Data Log By Machine Learning
    Pasupuleti, Venkat Rao
    Uhasri
    Kalyan, Pavan
    Srikanth
    Reddy, Hari Kiran
    [J]. 2020 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATION SYSTEMS (ICACCS), 2020, : 1395 - 1399
  • [4] Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
    Iskandaryan, Ditsuhi
    Ramos, Francisco
    Trilles, Sergio
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (07):
  • [5] Prediction of quality parameters of a dry air separation product using machine learning methods
    Zogala, Alina
    Rzychon, Maciej
    [J]. GOSPODARKA SUROWCAMI MINERALNYMI-MINERAL RESOURCES MANAGEMENT, 2019, 35 (02): : 119 - 138
  • [6] Machine Learning-Based Prediction of Air Quality
    Liang, Yun-Chia
    Maimury, Yona
    Chen, Angela Hsiang-Ling
    Juarez, Josue Rodolfo Cuevas
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (24): : 1 - 17
  • [7] Air Quality Index (AQI) Prediction in Holy Makkah Based on Machine Learning Methods
    Almaliki, Abdulrazak H.
    Derdour, Abdessamed
    Ali, Enas
    [J]. SUSTAINABILITY, 2023, 15 (17)
  • [8] Protein Secondary Structural Class Prediction using Effective Feature Modeling and Machine Learning Techniques
    Bankapur, Sanjay
    Patil, Nagamma
    [J]. PROCEEDINGS 2018 IEEE 18TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE), 2018, : 18 - 21
  • [9] IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon
    Signing, Vitrice Ruben Folifack
    Taamte, Jacob Mbarndouka
    Noube, Michaux Kountchou
    Yerima, Abba Hamadou
    Azzopardi, Joel
    Siaka, Yvette Flore Tchuente
    Saidou
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2024, 196 (07)
  • [10] Water quality prediction using machine learning methods
    Haghiabi, Amir Hamzeh
    Nasrolahi, Ali Heidar
    Parsaie, Abbas
    [J]. WATER QUALITY RESEARCH JOURNAL OF CANADA, 2018, 53 (01): : 3 - 13