Modelling Current and Future Mangrove Distribution under RCP 8.5 Climate Scenario: A Machine Learning Approach on Lombok Island, Indonesia
DOI:
10.65622/ijtb.v2i1.235Downloads
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.
Keywords:
Mangrove distribution Habitat suitability Machine learning Random Forest Climate changeReferences
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