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 asses

SUBMITTER: Yang EJ 

PROVIDER: S-EPMC10696195 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature