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All Journal Indenesian Journal of Tropical Biology Indenesian Journal of Tropical Biology
Zulkurniawan, Muhamad
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Environmental Science Health Professions Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences

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Modelling Current and Future Mangrove Distribution under RCP 8.5 Climate Scenario: A Machine Learning Approach on Lombok Island, Indonesia Purnama, Miftahul Irsyadi; Azizah, Lutfia; Grendis, Nuraqila Waida Bintang; Zulkurniawan, Muhamad; ÇOBAN, Hüseyin Oğuz
Indonesian Journal of Tropical Biology Vol. 2 No. 1 (2026): April (In Progrees)
Publisher : Yayasan Siti Widhatul Faeha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65622/ijtb.v2i1.235

Abstract

Mangrove ecosystems are essential for coastal protection and biodiversity, yet their distribution is highly influenced by climate variability. This study aims to predict current and future distribution of mangrove habitats on Lombok Island-Indonesia, using environmental predictor variables derived from topographic data and Köppen–Geiger climate classification. Mangrove distribution data were classified into presence and absence categories and integrated with climatic and terrain variables to develop habitat suitability models using five machine learning algorithms: Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Model performance was evaluated using accuracy metrics, and the best-performing model was selected for spatial projection under the Representative Concentration Pathway (RCP) 8.5 scenario for 2050 and 2080. The RF model showed the highest predictive performance. The results indicate a substantial decline in suitable mangrove habitats, decreasing from 12,443 ha under current conditions to 7,255 ha in 2050 and 6,336 ha in 2080, representing a reduction of nearly 50%. This decline is associated with changes in precipitation and temperature regimes that influence hydrological conditions and habitat suitability. The application of machine learning provides a robust spatial approach for predicting mangrove distribution and supports conservation planning and climate-adaptive coastal management.
Modelling Current and Future Mangrove Distribution under RCP 8.5 Climate Scenario: A Machine Learning Approach on Lombok Island, Indonesia Purnama, Miftahul Irsyadi; Azizah, Lutfia; Grendis, Nuraqila Waida Bintang; Zulkurniawan, Muhamad; ÇOBAN, Hüseyin Oğuz
Indonesian Journal of Tropical Biology Vol. 2 No. 1 (2026): April (In Progrees)
Publisher : Yayasan Siti Widhatul Faeha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65622/ijtb.v2i1.235

Abstract

Mangrove ecosystems are essential for coastal protection and biodiversity, yet their distribution is highly influenced by climate variability. This study aims to predict current and future distribution of mangrove habitats on Lombok Island-Indonesia, using environmental predictor variables derived from topographic data and Köppen–Geiger climate classification. Mangrove distribution data were classified into presence and absence categories and integrated with climatic and terrain variables to develop habitat suitability models using five machine learning algorithms: Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Model performance was evaluated using accuracy metrics, and the best-performing model was selected for spatial projection under the Representative Concentration Pathway (RCP) 8.5 scenario for 2050 and 2080. The RF model showed the highest predictive performance. The results indicate a substantial decline in suitable mangrove habitats, decreasing from 12,443 ha under current conditions to 7,255 ha in 2050 and 6,336 ha in 2080, representing a reduction of nearly 50%. This decline is associated with changes in precipitation and temperature regimes that influence hydrological conditions and habitat suitability. The application of machine learning provides a robust spatial approach for predicting mangrove distribution and supports conservation planning and climate-adaptive coastal management.
Co-Authors Azizah, Lutfia Çoban, Hüseyin Oğuz Grendis, Nuraqila Waida Bintang Purnama, Miftahul Irsyadi