Sustainable development goal 6 monitoring through statistical machine learning – Random Forest method

Abstract

Global reports from the United Nations project significant deficits in achieving water and sanitation targets by 2030, emphasizing the need for advanced methodologies in ecosystem monitoring. This study examines the integration of the Random Forest machine learning algorithm with freely available satellite imagery and open-source tools to monitor Permanent Protected Areas (PPAs) in the Distrito Federal, Brazil, contributing to Sustainable Development Goal (SDG) 6, which prioritizes clean water and sanitation. The research adopts a methodological approach that classifies land use changes within PPAs, with a focus on riparian zones along riverbanks, utilizing high-resolution Sentinel-2 satellite data processed through the Google Earth Engine platform. The findings indicate a 6% increase in native vegetation within PPAs from 2015 to 2022, highlighting the utility of machine learning technologies in environmental monitoring. The Random Forest algorithm demonstrated robust performance, with classification accuracy rates ranging from 83% to 88% and Kappa coefficients between 0.73 and 0.84. These results underscore the method's ability to enhance data granularity and reliability, supporting informed decision-making in ecosystem management. This research contributes to advancements in environmental monitoring methodologies and aligns with international efforts to achieve SDG targets. Further studies should investigate the incorporation of additional machine learning models to improve monitoring accuracy and support sustainable development initiatives.