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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.10317 |
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| _version_ | 1866911890546884608 |
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| author | Mazumder, Anirudh Engala, Sarthak Nallaparaju, Aditya |
| author_facet | Mazumder, Anirudh Engala, Sarthak Nallaparaju, Aditya |
| contents | Urbanization enables economic growth but also harms the environment through degradation. Traditional methods of detecting environmental issues have proven inefficient. Machine learning has emerged as a promising tool for tracking environmental deterioration by identifying key predictive features. Recent research focused on developing a predictive model using pollutant levels and particulate matter as indicators of environmental state in order to outline challenges. Machine learning was employed to identify patterns linking areas with worse conditions. This research aims to assist governments in identifying intervention points, improving planning and conservation efforts, and ultimately contributing to sustainable development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_10317 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Towards Sustainable Development: A Novel Integrated Machine Learning Model for Holistic Environmental Health Monitoring Mazumder, Anirudh Engala, Sarthak Nallaparaju, Aditya Machine Learning Urbanization enables economic growth but also harms the environment through degradation. Traditional methods of detecting environmental issues have proven inefficient. Machine learning has emerged as a promising tool for tracking environmental deterioration by identifying key predictive features. Recent research focused on developing a predictive model using pollutant levels and particulate matter as indicators of environmental state in order to outline challenges. Machine learning was employed to identify patterns linking areas with worse conditions. This research aims to assist governments in identifying intervention points, improving planning and conservation efforts, and ultimately contributing to sustainable development. |
| title | Towards Sustainable Development: A Novel Integrated Machine Learning Model for Holistic Environmental Health Monitoring |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2308.10317 |