Artificial Intelligence (AI) has emerged as a transformative tool in water quality modeling, revolutionizing how we comprehend and manage aquatic systems. By harnessing AI algorithms, such as machine learning and neural networks, intricate patterns within complex water quality data can be deciphered swiftly and accurately. Additionally, AI facilitates adaptive modeling, continually learning from new data inputs to refine its predictions and adapt to changing environmental conditions. AI applications in water quality modeling enable predictive analytics, identifying contaminant sources, real-time monitoring, and early detection of anomalies effectively, and facilitate the development of adaptive strategies for preserving water quality, ultimately contributing to more efficient and sustainable water resource management.

We welcome motivated researchers to submit high-quality studies employing AI-driven models for analyzing diverse data sources related to aquatic systems, incorporating real-time sensor readings, historical data, weather patterns, and geographical information for predicting water quality parameters, foreseeing potential contaminant levels or identifying pollution sources, enabling real-time monitoring, evolving early warning systems for potential contamination events, revealing correlations between various factors contributing to aquatic water quality, efficient and cost-effective waste treatment, and developing decision support tools for policy making to this article collection. This collection aims to enhance the scientific approaches by improving accuracy, enhancing response to contamination, and offering more profound insights into aquatic system dynamics, thereby advancing the research field of modeling and monitoring water quality.

Applications of AI in modeling water quality in various natural and engineered systems, such as rivers, estuaries, lakes, aquifers, drinking water distribution systems, and sewerage systems, are of interest. We welcome papers based on practical research in field and lab test facilities. We also welcome papers on novel AI-based modeling approaches.

A list of topics covered is given below:

• Role of AI in water quality modeling

• Evolution and trends in the integration of AI with water quality assessment

• Integrating diverse data sources (satellite imagery, sensor networks, weather data) using AI for comprehensive water quality modeling

• Advantages and challenges of data fusion in water quality assessment

• Implementation of AI algorithms for real-time monitoring of water quality

• Adaptive control systems using AI to respond to changes in water quality parameters

• AI-driven optimization of water treatment processes

• Integration of sensor networks and AI for rapid response to water quality threats

• Application of AI for uncertainty and sensitivity analysis in water quality modeling

• Assessing the reliability and robustness of AI-based water quality predictions

• Real-world examples of successful applications of AI in water quality modeling

• Lessons learned and best practices from implemented AI solutions

• Ethical issues related to AI in water quality assessment

• Addressing challenges such as bias, transparency, and accountability

• Combining IoT technologies with AI for enhanced water quality monitoring

• IoT sensor networks and their role in feeding data to AI models

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Frontiers in Water

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