Utilizing deep learning models for accurate prediction of air pollution levels Article Swipe
Harshit Jain
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.33545/26633582.2023.v5.i2a.91
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.33545/26633582.2023.v5.i2a.91
Air pollution has become a critical environmental issue, adversely affecting human health and the overall well-being of ecosystems. Accurate forecasting of air pollution levels is crucial for effective pollution management and mitigation strategies. In this study, we propose a deep learning-based model for air pollution forecasting that harnesses the power of neural networks to predict pollutant concentrations with high precision. Our approach involves training a deep learning model using historical air quality data, meteorological variables, and other relevant features. We leverage the temporal and spatial dependencies within the data to capture complex patterns and relationships. By incorporating information such as pollutant levels from previous time steps, meteorological conditions, and geographical factors, our model learns to effectively forecast air pollution levels. To train the deep learning model, we utilize a large dataset comprising historical air quality measurements from diverse monitoring stations. We preprocess the data, handle missing values, and normalize the features to ensure optimal training performance. The model architecture consists of multiple layers of interconnected neurons, enabling it to learn hierarchical representations of the input data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.33545/26633582.2023.v5.i2a.91
- https://www.computersciencejournals.com/ijecs/article/view/91/5-1-15
- OA Status
- bronze
- Cited By
- 1
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385709881
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385709881Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.33545/26633582.2023.v5.i2a.91Digital Object Identifier
- Title
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Utilizing deep learning models for accurate prediction of air pollution levelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-01Full publication date if available
- Authors
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Harshit JainList of authors in order
- Landing page
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https://doi.org/10.33545/26633582.2023.v5.i2a.91Publisher landing page
- PDF URL
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https://www.computersciencejournals.com/ijecs/article/view/91/5-1-15Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://www.computersciencejournals.com/ijecs/article/view/91/5-1-15Direct OA link when available
- Concepts
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Leverage (statistics), Air quality index, Deep learning, Computer science, Air pollution, Artificial neural network, Machine learning, Pollution, Artificial intelligence, Pollutant, Data mining, Meteorology, Geography, Chemistry, Organic chemistry, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.strategies. | 32 |
| abstract_inverted_index.architecture | 158 |
| abstract_inverted_index.dependencies | 85 |
| abstract_inverted_index.geographical | 109 |
| abstract_inverted_index.hierarchical | 170 |
| abstract_inverted_index.measurements | 135 |
| abstract_inverted_index.performance. | 155 |
| abstract_inverted_index.environmental | 6 |
| abstract_inverted_index.incorporating | 96 |
| abstract_inverted_index.concentrations | 56 |
| abstract_inverted_index.interconnected | 164 |
| abstract_inverted_index.learning-based | 40 |
| abstract_inverted_index.meteorological | 73, 106 |
| abstract_inverted_index.relationships. | 94 |
| abstract_inverted_index.representations | 171 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5084887810 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I212738717 |
| citation_normalized_percentile.value | 0.44484475 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |