A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula
<p dir="ltr">Accurate forecasting of environmental pollution indicators holds significant importance in diverse fields, including climate modeling, environmental monitoring, and public health. In this study, we investigate a wide range of machine learning and deep learning models to...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513543055867904 |
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| author | Ahmad Qadeib Alban (16855206) |
| author2 | Ammar Abulibdeh (15785928) Lanouar Charfeddine (10705000) Rawan Abulibdeh (19206115) Abdelgadir Abuelgasim (21841442) |
| author2_role | author author author author |
| author_facet | Ahmad Qadeib Alban (16855206) Ammar Abulibdeh (15785928) Lanouar Charfeddine (10705000) Rawan Abulibdeh (19206115) Abdelgadir Abuelgasim (21841442) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad Qadeib Alban (16855206) Ammar Abulibdeh (15785928) Lanouar Charfeddine (10705000) Rawan Abulibdeh (19206115) Abdelgadir Abuelgasim (21841442) |
| dc.date.none.fl_str_mv | 2024-04-29T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s41748-024-00398-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Comprehensive_Machine_and_Deep_Learning_Approach_for_Aerosol_Optical_Depth_Forecasting_New_Evidence_from_the_Arabian_Peninsula/29714987 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Earth sciences Atmospheric sciences Environmental sciences Environmental management Health sciences Public health Information and computing sciences Machine learning Aerosol optical depth Time series forecasting Deseasonalization Machine and deep learning models Feature extraction |
| dc.title.none.fl_str_mv | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Accurate forecasting of environmental pollution indicators holds significant importance in diverse fields, including climate modeling, environmental monitoring, and public health. In this study, we investigate a wide range of machine learning and deep learning models to enhance Aerosol Optical Depth (AOD) predictions for the Arabian Peninsula (AP) region, one of the world’s main dust source regions. Additionally, we explore the impact of feature extraction and their different types on the forecasting performance of each of the proposed models. Preprocessing of the data involves inputting missing values, data deseasonalization, and data normalization. Subsequently, hyperparameter optimization is performed on each model using grid search. The empirical results of the basic, hybrid and combined models revealed that the convolutional long short-term memory and Bayesian ridge models significantly outperformed the other basic models. Moreover, for the combined models, specifically the weighted averaging scheme, exhibit remarkable predictive accuracy, outperforming individual models and demonstrating superior performance in longer-term forecasts. Our findings emphasize the efficacy of combining distinct models and highlight the potential of the convolutional long short-term memory and Bayesian ridge models for univariate time series forecasting, particularly in the context of AOD predictions. These accurate daily forecasts bear practical implications for policymakers in various areas such as tourism, transportation, and public health, enabling better planning and resource allocation.</p><h2>Other Information</h2><p dir="ltr">Published in: Earth Systems and Environment<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s41748-024-00398-w" target="_blank">https://dx.doi.org/10.1007/s41748-024-00398-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_db1ea088f8693990fa0ff1b2372e90d7 |
| identifier_str_mv | 10.1007/s41748-024-00398-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29714987 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian PeninsulaAhmad Qadeib Alban (16855206)Ammar Abulibdeh (15785928)Lanouar Charfeddine (10705000)Rawan Abulibdeh (19206115)Abdelgadir Abuelgasim (21841442)Earth sciencesAtmospheric sciencesEnvironmental sciencesEnvironmental managementHealth sciencesPublic healthInformation and computing sciencesMachine learningAerosol optical depthTime series forecastingDeseasonalizationMachine and deep learning modelsFeature extraction<p dir="ltr">Accurate forecasting of environmental pollution indicators holds significant importance in diverse fields, including climate modeling, environmental monitoring, and public health. In this study, we investigate a wide range of machine learning and deep learning models to enhance Aerosol Optical Depth (AOD) predictions for the Arabian Peninsula (AP) region, one of the world’s main dust source regions. Additionally, we explore the impact of feature extraction and their different types on the forecasting performance of each of the proposed models. Preprocessing of the data involves inputting missing values, data deseasonalization, and data normalization. Subsequently, hyperparameter optimization is performed on each model using grid search. The empirical results of the basic, hybrid and combined models revealed that the convolutional long short-term memory and Bayesian ridge models significantly outperformed the other basic models. Moreover, for the combined models, specifically the weighted averaging scheme, exhibit remarkable predictive accuracy, outperforming individual models and demonstrating superior performance in longer-term forecasts. Our findings emphasize the efficacy of combining distinct models and highlight the potential of the convolutional long short-term memory and Bayesian ridge models for univariate time series forecasting, particularly in the context of AOD predictions. These accurate daily forecasts bear practical implications for policymakers in various areas such as tourism, transportation, and public health, enabling better planning and resource allocation.</p><h2>Other Information</h2><p dir="ltr">Published in: Earth Systems and Environment<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s41748-024-00398-w" target="_blank">https://dx.doi.org/10.1007/s41748-024-00398-w</a></p>2024-04-29T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s41748-024-00398-whttps://figshare.com/articles/journal_contribution/A_Comprehensive_Machine_and_Deep_Learning_Approach_for_Aerosol_Optical_Depth_Forecasting_New_Evidence_from_the_Arabian_Peninsula/29714987CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297149872024-04-29T09:00:00Z |
| spellingShingle | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula Ahmad Qadeib Alban (16855206) Earth sciences Atmospheric sciences Environmental sciences Environmental management Health sciences Public health Information and computing sciences Machine learning Aerosol optical depth Time series forecasting Deseasonalization Machine and deep learning models Feature extraction |
| status_str | publishedVersion |
| title | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula |
| title_full | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula |
| title_fullStr | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula |
| title_full_unstemmed | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula |
| title_short | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula |
| title_sort | A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula |
| topic | Earth sciences Atmospheric sciences Environmental sciences Environmental management Health sciences Public health Information and computing sciences Machine learning Aerosol optical depth Time series forecasting Deseasonalization Machine and deep learning models Feature extraction |