EXPLORING PUBLIC VIEWS ON OCEANIC AND SHORELINE ECOSYSTEM THREATS THROUGH MACHINE LEARNING

The study employed data from the publicly accessible dataset titled "Marine and Coastal Ecosystems and Climate Change: Dataset from a Public Awareness Survey" to investigate public perceptions regarding significant dangers to oceanic and shoreline ecosystems. Survey participants assessed the severity of 13 notable threats, such as fishing, plastic pollution, climate change, shipping, and coastal development, using a psychometric rating scale of 1 (very low) to 5 (very high), with an additional option for "I don't know." The primary aim of the study was to classify respondents through principal component analysis (PCA)-based k-means clustering. This analysis identified three distinct participant clusters, each reflecting varying perspectives on the severity of these threats. This PCA-based k-means clustering offered crucial insights into the diverse ways different population segments perceive and prioritize environmental risks. By mapping these perceptions, the findings provided valuable guidance for policymakers, emphasizing the necessity for customized, data-driven strategies in environmental conservation and marine protection. These insights can bolster efforts to align public concern with effective climate change mitigation and targeted interventions to safeguard marine ecosystems.