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Atmosphere
Special Issues
Atmospheric Modeling with Artificial Intelligence Technologies
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Atmospheric Modeling with Artificial Intelligence Technologies

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 10 October 2025 | Viewed by 534

Special Issue Editors

Dr. Wei Tang

E-Mail
Guest Editor
Atmospheric Environment Institute, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Interests: air quality modeling; inverse model; satellite data; source apportionment
Special Issues, Collections and Topics in MDPI journals
Dr. Fan Meng

E-Mail Website
Guest Editor
Asia Center for Air Pollution Research, Niigata 950-2144, Japan
Interests: atmospheric modeling and forecasting; emission inventory; acid rain; source apportionment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High economic growth in recent years, along with fast industrialization, urbanization, and motorization, as well as agricultural activities, has enhanced emissions, resulting in the degradation of air quality and negative impacts on human health and climate. Applications of artificial intelligence (AI) techniques recently, such as big data processing and analysis, machine learning, and deep learning, can handle large volumes of atmospheric data from various sources, identify and correct errors or missing values, and therefore improve model performance and accuracy significantly. The aim of this Special Issue is to provide recent research activities in the field of atmospheric modeling that incorporate big data and deep learning technologies, including meteorological and air quality predictions, source apportionment modeling, the parameterization of atmospheric physical and chemical processes, and uncertainty quantification and inverse modeling.

Topics of interest for the Special Issue include, but are not limited to, the following:

  • Air quality modeling;
  • Source apportionment analysis and control policies;
  • The applications of big data in air quality analysis;
  • The applications of big data and deep learning technologies in atmospheric modeling.

Dr. Wei Tang
Dr. Fan Meng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air quality modeling and prediction
  • source apportionment
  • inverse modeling
  • big data analysis
  • deep learning technology

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Published Papers (1 paper)

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Research

32 pages, 7263 KiB  
Article
Time Series Prediction and Modeling of Visibility Range with Artificial Neural Network and Hybrid Adaptive Neuro-Fuzzy Inference System
by Okikiade Adewale Layioye, Pius Adewale Owolawi and Joseph Sunday Ojo
Atmosphere 2025, 16(8), 928; https://doi.org/10.3390/atmos16080928 - 31 Jul 2025
Viewed by 310
Abstract
The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) [...] Read more.
The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R2), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R2 for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
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