Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review
Biodiversity projections and model evaluation are essential to inform future formulation of biodiversity policy. These could be supported by data analytics and machine learning approaches, as well as precision technologies. However, existing works are segregated by the selection of species under-stu...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/37816/ |
| _version_ | 1848848707887300608 |
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| author | Mohd Sharef, Nurfadhlina Nasharuddin, Nurul Amelina Mohamed, Raihani Zamani, Nabila Wardah Osman, Mohd Hafeez Yaakob, Razali |
| author_facet | Mohd Sharef, Nurfadhlina Nasharuddin, Nurul Amelina Mohamed, Raihani Zamani, Nabila Wardah Osman, Mohd Hafeez Yaakob, Razali |
| author_sort | Mohd Sharef, Nurfadhlina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Biodiversity projections and model evaluation are essential to inform future formulation of biodiversity policy. These could be supported by data analytics and machine learning approaches, as well as precision technologies. However, existing works are segregated by the selection of species under-study and depending on the location. This paper reviews the existing approaches for precision biodiversity covering dashboard and data analytics, deep learning and machine learning, and digital twin for precision biodiversity. We propose a framework based on interactive machine learning that could facilitate a continuous biodiversity projection modeling to facilitate incremental learning and reduce uncertainties from the complex factors that contribute to biodiversity declines. The proposed framework exploits digital twin model based on a research forest setting that pioneers this work in Malaysia. The framework comprises of short-term quick wins and long-term expectation of digitalization transformation towards precision biodiversity. |
| first_indexed | 2025-11-15T09:38:47Z |
| format | Conference or Workshop Item |
| id | upm-37816 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:38:47Z |
| publishDate | 2022 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-378162023-11-07T09:36:11Z http://psasir.upm.edu.my/id/eprint/37816/ Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review Mohd Sharef, Nurfadhlina Nasharuddin, Nurul Amelina Mohamed, Raihani Zamani, Nabila Wardah Osman, Mohd Hafeez Yaakob, Razali Biodiversity projections and model evaluation are essential to inform future formulation of biodiversity policy. These could be supported by data analytics and machine learning approaches, as well as precision technologies. However, existing works are segregated by the selection of species under-study and depending on the location. This paper reviews the existing approaches for precision biodiversity covering dashboard and data analytics, deep learning and machine learning, and digital twin for precision biodiversity. We propose a framework based on interactive machine learning that could facilitate a continuous biodiversity projection modeling to facilitate incremental learning and reduce uncertainties from the complex factors that contribute to biodiversity declines. The proposed framework exploits digital twin model based on a research forest setting that pioneers this work in Malaysia. The framework comprises of short-term quick wins and long-term expectation of digitalization transformation towards precision biodiversity. IEEE 2022 Conference or Workshop Item PeerReviewed Mohd Sharef, Nurfadhlina and Nasharuddin, Nurul Amelina and Mohamed, Raihani and Zamani, Nabila Wardah and Osman, Mohd Hafeez and Yaakob, Razali (2022) Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review. In: 2022 International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS), 23 Nov. 2022, Bandung, Indonesia. . https://ieeexplore.ieee.org/document/10055149 10.1109/ICACNIS57039.2022.10055149 |
| spellingShingle | Mohd Sharef, Nurfadhlina Nasharuddin, Nurul Amelina Mohamed, Raihani Zamani, Nabila Wardah Osman, Mohd Hafeez Yaakob, Razali Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review |
| title | Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review |
| title_full | Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review |
| title_fullStr | Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review |
| title_full_unstemmed | Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review |
| title_short | Applications of data analytics and machine learning for digital twin-based precision biodiversity: a review |
| title_sort | applications of data analytics and machine learning for digital twin-based precision biodiversity: a review |
| url | http://psasir.upm.edu.my/id/eprint/37816/ http://psasir.upm.edu.my/id/eprint/37816/ http://psasir.upm.edu.my/id/eprint/37816/ |