AI-Based Advancements for Comprehensive Mangrove Analysis Suitability Mapping

Abstract

Habitat loss remains a critical global issue linked to climate change. The Philippines, recognized as a global hotspot for marine biodiversity, is home to extensive mangrove forests. Despite their ecological significance, these forests are increasingly threatened by deforestation and various anthropogenic pressures. To support conservation efforts and mitigate habitat loss, this study employs habitat suitability modeling to identify optimal afforestation zones for mangrove species. Machine learning algorithms, including Maximum Entropy (MaxEnt), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), were utilized to analyze mangrove distribution. A total of 17 environmental factors, encompassing bioclimatic and marine variables, along with mangrove sapling presence points, were integrated into the models. Experimental results demonstrated that the RF model outperformed others, achieving an area under the curve (AUC) value of 0.97 in predicting potential mangrove habitats. Based on RF model predictions, the model identified high-suitability zones for mangrove restoration and afforestation. The generated suitability maps provide valuable insights for developing science-based adaptation policies, strategies, and measures aimed at enhancing the resilience of mangrove ecosystems against present and future climate challenges.