Work

Operational evidence standards for machine learning in wastewater treatment

Year: 2026

Type: article

Abstract: Machine learning studies in water treatment often report high retrospective accuracy but rarely show whether models can support operational decisions. We analyse 423 full-text studies and a bounded six-plant wastewater-treatment scenario. Plant deployment (2.8%), real-time testing (5.2%), future-facing validation (18.2%) and uncertainty reporting (8.5%) remain uncommon. We propose an operation-aware evidence standard linking sensing context, validation timing, uncertainty and control role to deployment claims.

Source: npj Clean Water

DOI: 10.1038/s41545-026-00610-6

Authors: Siyuan Jiang, Ying Yang, Keqiang Zhang, Yaoru Mao, Xiuwen Cheng

Institution: Lanzhou University

Language: English

Topic: Water Quality Monitoring and Analysis

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,890