Machine learning to support citizen science in urban environmental management

Abstract

Machine learning (ML) and citizen science (CS) are increasingly prevalent and rapidly evolving approaches to studying and managing environmental challenges. Municipal and other governance actors can benefit from technology advances in ML and public engagement benefits of CS but must also address validity and other quality assurance concerns in their application to particular management contexts. In this article, we take up the pervasive challenge of urban litter to demonstrate how ML can support CS by providing quality assurance in the regulatory context of California's stormwater program. We gave quantitative CS-collected data to five ML models to compare their predictions of a qualitative, site-specific, multiclass “Litter Index” score, an important regulatory metric typically only assessed by trained experts. XGBoost had the best outcome, with scores of 0.98 for accuracy, precision, recall and F-1. These strong results show that ML can provide a reliable complement to CS assessments and increase quality assurance in a regulatory context. To date, ML and CS have each contributed to litter management in novel ways and we find that their integration can provide important synergies with additional applications in other environmental management domains.