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Atmosphere
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Novel Algorithms and Advanced Computing Methods Application in Atmosphere
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Novel Algorithms and Advanced Computing Methods Application in Atmosphere

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

Deadline for manuscript submissions: 25 February 2025 | Viewed by 424

Special Issue Editors

Dr. Rana Muhammad Adnan

E-Mail Website
Guest Editor
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Interests: sustainable development; water resources management; hydrological modeling; artificial intelligence; time series analysis; rainfall–runoff relationship; wind energy; sediment load; evaporation; evapotranspiration; hydro-meteorological droughts; groundwater; water quality parameters modeling; novel meta-heuristic approaches applications; trend analysis; clustering; watershed planning and management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ozgur Kisi

grade E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Natural Sciences and Engineering Ilia State University, 0162 Tbilisi, Georgia
Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling; suspended sediment modeling; forecasting; estimating; spatial and temporal analysis of hydro-climatic variables such as precipitation; streamflow; suspended sediment; evaporation; evapotranspiration; groundwater; lake level and water quality parameters; hydro-informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Mo Wang

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Guangzhou University, Guangzhou, China
Interests: green infrastructure; low impact development; landscape planning; stormwater management; nature-based solution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will feature the latest advances and developments in sustainable atmospheric management. The focus is centered on advanced computing methods and new optimization algorithm methods for forecasting atmospheric variables to achieve an optimal sustainable atmosphere. The current computational power available allows us to tackle simulation challenges in atmospheric modeling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, the design or optimization of hydraulic structures, the calibration of model parameters, uncertainty quantification, and real-time model-based control. New algorithms and advanced computing methods are useful in the prediction requirements of atmospheric data, including atmospheric river prediction, the risk prediction of atmospheric emissions, turbulence and hazard prediction, the class prediction of atmospheric circulation patterns, the prediction of geothermal heat flux, air quality monitoring, rainfall prediction, atmospheric aerosol prediction, global weather prediction systems, the prediction of the influence of atmospheric parameters on human health, etc.

The main themes of this Special Issue include but are not limited to the following: 

  • Application of advanced computing methods, including machine learning and deep learning, for precise atmospheric variable forecasting (modeling rainfall, air quality, flood, atmospheric aerosol prediction, solar radiation, wind speed, air temperature, evaporation, evapotranspiration, etc.).
  • Utilization of advanced machine learning and deep learning models with ensemble models for solving atmospheric problems.
  • Spatial and temporal modeling of atmospheric variables with the aid of advanced computing models.
  • Coupling of data preprocessing techniques with machine learning and deep learning methods to capture noise and nonlinear atmospheric variables.
  • Usage and development of novel optimization algorithms with machine learning methods to enhance their computing abilities.

Dr. Rana Muhammad Adnan
Prof. Dr. Ozgur Kisi
Dr. Mo Wang
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

  • advanced computing methods
  • machine learning
  • deep learning
  • algorithms
  • rainfall
  • air quality
  • flood
  • atmospheric aerosol prediction
  • solar radiation
  • wind speed
  • air temperature
  • evaporation
  • evapotranspiration

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Published Papers (1 paper)

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Research

25 pages, 4564 KiB  
Article
Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds
by Rana Muhammad Adnan, Wang Mo, Ozgur Kisi, Salim Heddam, Ahmed Mohammed Sami Al-Janabi and Mohammad Zounemat-Kermani
Atmosphere 2024, 15(12), 1407; https://doi.org/10.3390/atmos15121407 - 22 Nov 2024
Abstract
Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The [...] Read more.
Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R2). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R2 (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. Full article
(This article belongs to the Special Issue Novel Algorithms and Advanced Computing Methods Application in Atmosphere)
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