About this Research Topic
Artificial intelligence (AI) is rapidly emerging as a transformative tool in environmental science, particularly in addressing water contamination—a critical threat to global sustainability. Contaminated water not only poses severe health risks but also jeopardizes the ecological balance, affecting biodiversity and ecosystem services. Recent studies leverage AI to integrate and analyze vast datasets from sensors and satellite imagery to track water quality issues in real-time. This approach has started to revolutionize how we monitor ecosystems and manage environmental hazards, yet the integration of AI into widespread water management practices remains in its nascent stages. Despite promising developments, the potential of AI to systematically predict and mitigate water contamination challenges has not been fully tapped.
This Research Topic aims to thoroughly assess AI's capacity to address water contamination within an environmental sustainability framework. The primary objective is to explore the efficacy of AI in enhancing water purity, ensuring efficient resource utilization, and contributing to the conservation of aquatic ecosystems. By examining cutting-edge research and real-world applications, the collection will underscore AI's role in fostering a more sustainable management approach to our planet's water resources.
The proposed research topic is aimed at understanding the multifaceted applications of AI in water quality management and sustainability, focusing on both surface and groundwater contamination. To gather further insights into AI's applications in this field, we welcome articles addressing, but not limited to, the following themes:
• Environmental monitoring and data analysis using AI;
• AI-driven modeling and prediction for ecosystem management;
• AI-based optimization techniques for sustainable resource allocation;
• Decision support systems integrating AI for environmental policy-making;
• AI-enabled technologies for water quality and contamination prediction;
• Enhancing water management through AI-based techniques.
This ambitious investigation invites contributions from a diverse spectrum of academic and professional backgrounds, blending expertise from environmental science, computer science, engineering, ecology, sustainability, and policy-making to foster an interdisciplinary understanding and innovation in sustainable water management.
This Research Topic aims to thoroughly assess AI's capacity to address water contamination within an environmental sustainability framework. The primary objective is to explore the efficacy of AI in enhancing water purity, ensuring efficient resource utilization, and contributing to the conservation of aquatic ecosystems. By examining cutting-edge research and real-world applications, the collection will underscore AI's role in fostering a more sustainable management approach to our planet's water resources.
The proposed research topic is aimed at understanding the multifaceted applications of AI in water quality management and sustainability, focusing on both surface and groundwater contamination. To gather further insights into AI's applications in this field, we welcome articles addressing, but not limited to, the following themes:
• Environmental monitoring and data analysis using AI;
• AI-driven modeling and prediction for ecosystem management;
• AI-based optimization techniques for sustainable resource allocation;
• Decision support systems integrating AI for environmental policy-making;
• AI-enabled technologies for water quality and contamination prediction;
• Enhancing water management through AI-based techniques.
This ambitious investigation invites contributions from a diverse spectrum of academic and professional backgrounds, blending expertise from environmental science, computer science, engineering, ecology, sustainability, and policy-making to foster an interdisciplinary understanding and innovation in sustainable water management.
Keywords: Artificial Intelligence, Environmental Sustainability, Machine Learning, Deep Learning, Generative AI, Data Science, Big Data, Water Contaminants, Heavy Metals
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
