Integrating Artificial Intelligence in Environmental Monitoring: A Paradigm Shift in Data-Driven Sustainability

Area of Science:

• Environmental Science
• Ecology
• Computer Science

Background:

• Traditional environmental monitoring methods are often resource-intensive and difficult to scale.
• Growing human impact necessitates advanced, efficient monitoring solutions.
• Artificial intelligence (AI) offers automated data collection, real-time analysis, and predictive capabilities.

Purpose of the Study:

• To review advancements in AI for environmental monitoring.
• To highlight AI applications in air/water quality, climate, and biodiversity.
• To discuss challenges and strategies for AI implementation in ecological stewardship.

Main Methods:

• Review of current AI techniques, including machine learning (ML) and deep learning (DL).
• Exploration of AI applications using drone imagery, camera traps, and GPS data.
• Emphasis on emerging technologies like explainable AI (XAI), edge computing, and the Internet of Things (IoT).

Main Results:

• AI enables automated data collection, real-time analysis, and predictive modeling for environmental monitoring.
• AI applications span species identification, habitat assessment, wildlife tracking, and anti-poaching efforts.
• Emerging technologies like XAI, edge computing, and IoT enhance AI model transparency and reduce costs.

Conclusions:

• AI holds transformative potential for sustainable environmental management and ecological stewardship.
• Addressing challenges like data quality, computational demands, and model interpretability is crucial.
• Interdisciplinary strategies, ethical frameworks, and policy alignment are key for successful AI integration.