Applications of Machine Learning and Artificial Intelligence in Water Technology
The world is facing an increasing array of challenges in water resource management, ranging from water scarcity and pollution to the deteriorating condition of infrastructure. In response to these challenges, Artificial Intelligence (AI) has emerged as a transformative force, offering innovative approaches to arrive at solutions and strategies to address complex issues in the water technology sector and revolutionize how we understand, manage, and optimize water-related processes.
Leveraging AI, including machine learning, data analytics, and advanced algorithms, researchers and professionals can harness the power of data along with domain knowledge to to make informed decisions, predict water-related phenomena, and enhance the overall efficiency and sustainability of water systems. These advancements are paving the way for smarter, more resilient, and eco-conscious water management practices that can impact a multitude of areas, from water resource allocation to purification, distribution, and environmental preservation.
The Journal of Water Technology invites researchers, scientists, engineers, and experts in the field to contribute to a special article collection on the “Applications of Machine Learning and Artificial Intelligence in Water Technology”.
We invite original research papers and review articles focusing on the application of machine learning approaches and AI in various aspects of water technology, including but not limited to:
- Smart Water Management: Decision support systems for water resource management, drought prediction, and flood control.
- Water Quality Monitoring: Solutions for real-time water quality assessment and pollution detection.
- Water Treatment and Purification: Optimization of water treatment processes, including filtration, desalination, and disinfection.
- Leak Detection and Infrastructure Management: Early detection of leaks and predictive maintenance of water distribution networks.
- Environmental Impact Assessment: AI applications for evaluating the environmental effects of water-related projects and policies.
- Data Analytics and Forecasting: AI and machine learning models for water consumption forecasting, weather predictions, and hydrological modeling.
- Sensors and IoT in Water Technology: The role of AI in integrating sensors and the Internet of Things (IoT) in water technology.
- Autonomous water systems: Self-adaptive operation of water treatment and desalination systems driven by integration of model-based control with machine learning based models and AI-driven decision operational support
- Case Studies and Best Practices: Real-world applications of AI in the water technology sector, including success stories and lessons learned.
Timeline, submission process and peer review
This article collection is now open for submission. Accepted manuscripts will be published ‘online first’ as soon as they are ready.
Please submit your work using our online submission system and select “AI Applications Collection” as the section/category.
If this is your first time submitting to JOWT, you will need to create an account through which you can upload your manuscript files and track your submission.
Following submission, all manuscripts will be assigned to a handling editor and subject to single-blind peer review. Final decisions will be made by the Editor-in-Chief.
Other Information
See JOWT’s submission guidelines here: https://www.worldscientific.com/page/jowt/submission-guidelines
For JOWT’s peer review policy, refer to https://www.worldscientific.com/page/authors/peer-review-policy