Constructed Wetlands in the Era of Artificial Intelligence: From Algorithm-Driven Processes to Intelligent Ecology

Abstract

Artificial intelligence (AI) is increasingly transforming constructed wetlands (CWs) from conventional treatment systems toward intelligent and adaptive environmental technologies. This review systematically evaluates the applications of AI in CWs using literature analysis and effect size analysis, with a focus on algorithm selection, treatment performance prediction, process optimization, and system assessment. The analysis identifies major applications of AI in CWs, including prediction of pollutant removal and treatment performance, optimization of wetland design and operational parameters, assessment of greenhouse gas emissions and emerging pollutants, interpretation of microbial community dynamics, and evaluation of wetland health. Different AI approaches show distinct advantages for addressing specific CW management challenges, highlighting the importance of matching algorithm characteristics with data types and process complexity. The integration of AI provides new opportunities to improve the prediction, optimization, monitoring, and adaptive management of CWs while enhancing understanding of complex biogeochemical processes. However, challenges related to data availability and quality, model interpretability, generalizability, and practical implementation remain. This review provides a framework for selecting and applying AI technologies in CWs and highlights priorities for developing more intelligent, reliable, and sustainable CW systems.