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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...

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Main Authors: Mohd Sharef, Nurfadhlina, Nasharuddin, Nurul Amelina, Mohamed, Raihani, Zamani, Nabila Wardah, Osman, Mohd Hafeez, Yaakob, Razali
Format: Conference or Workshop Item
Published: IEEE 2022
Online Access:http://psasir.upm.edu.my/id/eprint/37816/
https://ieeexplore.ieee.org/document/10055149
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
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.
format Conference or Workshop Item
author Mohd Sharef, Nurfadhlina
Nasharuddin, Nurul Amelina
Mohamed, Raihani
Zamani, Nabila Wardah
Osman, Mohd Hafeez
Yaakob, Razali
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
author_facet Mohd Sharef, Nurfadhlina
Nasharuddin, Nurul Amelina
Mohamed, Raihani
Zamani, Nabila Wardah
Osman, Mohd Hafeez
Yaakob, Razali
author_sort Mohd Sharef, Nurfadhlina
title 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_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_sort applications of data analytics and machine learning for digital twin-based precision biodiversity: a review
publisher IEEE
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/37816/
https://ieeexplore.ieee.org/document/10055149
_version_ 1783879902047502336
score 13.211869

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