Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia
Mangrove forests are crucial for carbon sequestration and biodiversity conservation but are threatened by anthropogenic effects and climate change. Although restoration efforts have been initiated, their effectiveness remains uncertain due to the absence of robust monitoring and evaluation mechanism...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/129/e3sconf_staclim2024_03002.pdf |
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| author | Abu Bakar Nurul Asyiqin Wan Mohd Jaafar Wan Shafrina Omar Hamdan Muhammad Nor Siti Mariam Muhmad Kamarulzaman Aisyah Marliza Kemarau Ricky Anak |
| author_facet | Abu Bakar Nurul Asyiqin Wan Mohd Jaafar Wan Shafrina Omar Hamdan Muhammad Nor Siti Mariam Muhmad Kamarulzaman Aisyah Marliza Kemarau Ricky Anak |
| author_sort | Abu Bakar Nurul Asyiqin |
| collection | DOAJ |
| description | Mangrove forests are crucial for carbon sequestration and biodiversity conservation but are threatened by anthropogenic effects and climate change. Although restoration efforts have been initiated, their effectiveness remains uncertain due to the absence of robust monitoring and evaluation mechanisms. This study investigates machine learning algorithms for modelling aboveground biomass (AGB) in mangrove forests across Peninsular Malaysia. Data on tree diameter at breast height (DBH) and species were collected in Sungai Pulai, Sungai Johor, and Sungai Merbok. Combined with remote sensing data, the performance of Extreme Gradient Boosting (XGBoost), Random Forest (RF), Boosted Tree (BT), and Support Vector Machine (SVM) was compared, chosen for their ability to analyze complex patterns and predict accurately. The results indicated that XGBoost outperformed the others by achieving higher R² values of 0.97, lower mean absolute error (MAE) of 6.80 Mg ha-¹, and root mean squared error (RMSE) of 26.74 Mg ha-¹, demonstrating superior accuracy and predictive performance. This study also shows similar above-ground carbon (AGC) values across the study areas and in comparison with previous studies. XGBoost’s robust capacity for estimating AGB and AGC highlights its potential to significantly improve mangrove forest management and conservation efforts on a broader scale. |
| format | Article |
| id | doaj-art-d10f2c0c6bac44828fa3dbdd7c846845 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-d10f2c0c6bac44828fa3dbdd7c8468452025-01-16T11:22:27ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015990300210.1051/e3sconf/202459903002e3sconf_staclim2024_03002Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular MalaysiaAbu Bakar Nurul Asyiqin0Wan Mohd Jaafar Wan Shafrina1Omar Hamdan2Muhammad Nor Siti Mariam3Muhmad Kamarulzaman Aisyah Marliza4Kemarau Ricky Anak5Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan MalaysiaEarth Observation Centre, Institute of Climate Change, Universiti Kebangsaan MalaysiaForest Research Institute MalaysiaFaculty of Science and Marine Environment, Universiti Malaysia TerengganuEarth Observation Centre, Institute of Climate Change, Universiti Kebangsaan MalaysiaEarth Observation Centre, Institute of Climate Change, Universiti Kebangsaan MalaysiaMangrove forests are crucial for carbon sequestration and biodiversity conservation but are threatened by anthropogenic effects and climate change. Although restoration efforts have been initiated, their effectiveness remains uncertain due to the absence of robust monitoring and evaluation mechanisms. This study investigates machine learning algorithms for modelling aboveground biomass (AGB) in mangrove forests across Peninsular Malaysia. Data on tree diameter at breast height (DBH) and species were collected in Sungai Pulai, Sungai Johor, and Sungai Merbok. Combined with remote sensing data, the performance of Extreme Gradient Boosting (XGBoost), Random Forest (RF), Boosted Tree (BT), and Support Vector Machine (SVM) was compared, chosen for their ability to analyze complex patterns and predict accurately. The results indicated that XGBoost outperformed the others by achieving higher R² values of 0.97, lower mean absolute error (MAE) of 6.80 Mg ha-¹, and root mean squared error (RMSE) of 26.74 Mg ha-¹, demonstrating superior accuracy and predictive performance. This study also shows similar above-ground carbon (AGC) values across the study areas and in comparison with previous studies. XGBoost’s robust capacity for estimating AGB and AGC highlights its potential to significantly improve mangrove forest management and conservation efforts on a broader scale.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/129/e3sconf_staclim2024_03002.pdf |
| spellingShingle | Abu Bakar Nurul Asyiqin Wan Mohd Jaafar Wan Shafrina Omar Hamdan Muhammad Nor Siti Mariam Muhmad Kamarulzaman Aisyah Marliza Kemarau Ricky Anak Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia E3S Web of Conferences |
| title | Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia |
| title_full | Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia |
| title_fullStr | Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia |
| title_full_unstemmed | Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia |
| title_short | Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia |
| title_sort | modelling above ground biomass using machine learning algorithms in mangrove forests of peninsular malaysia |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/129/e3sconf_staclim2024_03002.pdf |
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