AI (Gen) and Machine Learning Applications in the Water and Wastewater Sector
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Wastewater Treatment and Reuse".
Deadline for manuscript submissions: 25 June 2026 | Viewed by 56
Special Issue Editor
Interests: machine learning in water and wastewater treatment; digital twins and real-time control; energy and emissions optimization in WWTPs; data-driven and hybrid process modelling; water and wastewater quality prediction; leachate and solid waste management; groundwater and surface water modelling; emerging contaminants and microplastics
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI), including recent advances in generative AI (GenAI), together with machine learning (ML), is rapidly transforming the way we design, operate, and optimise water and wastewater treatment systems. From robust water quality prediction and soft-sensing of unmeasured variables to advanced control of biological and physico-chemical processes, AI and ML offer powerful tools to support more resilient, low-carbon, and cost-effective treatment solutions. However, important challenges remain regarding data quality and availability; model generalisation and interpretability; and the responsible use of GenAI, its integration with mechanistic models and digital twins, and its deployment in full-scale facilities under real-time constraints.
This Special Issue, “AI (Gen) and Machine Learning Applications in the Water and Wastewater Sector”, aims to bring together cutting-edge contributions that bridge theory and practice. We welcome original research and critical reviews on data-driven, physics-informed, hybrid, and GenAI-based approaches for drinking water treatment, wastewater and resource recovery facilities, water reuse, and related processes. Contributions may cover topics such as process modelling and optimisation, monitoring and anomaly detection, fouling and failure prediction, greenhouse gas and pollutant emissions, water quality forecasting, digital twins, and decision support tools.
By synthesizing recent advances in this field and showcasing successful applications of AI and ML at laboratory, pilot, and full scales, this Special Issue will provide a timely reference for researchers, practitioners, and utilities seeking to harness AI and ML for smarter, more sustainable water and wastewater treatment.
Dr. Taher Abunama
Guest Editor
Manuscript Submission Information
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Keywords
- AI (Gen)
- ML
- LLM
- digital twins
- wastewater treatment
- drinking water treatment
- process control and optimisation
- hybrid modelling
- water quality prediction
- anomaly detection and fault diagnosis
- energy and emissions reduction
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