This chapter explores the transformative role of AI-driven data analytics in
environmental decision-making, addressing its applications, methodologies, and critical
challenges. It examines how artificial intelligence enhances environmental monitoring,
pollution tracking, biodiversity conservation, and efforts to mitigate climate change.
The chapter delves into multimodal data analytics, examining the integration of diverse
data sources to provide comprehensive environmental insights. Key challenges,
including data quality, scalability, and interpretability, are analyzed alongside ethical
considerations, such as privacy and environmental equity. The chapter also presents
evaluation metrics for AI-driven environmental systems and explores emerging
solutions to improve data quality, model explainability, and governance frameworks.
Ultimately, it outlines future directions for AI in environmental decision-making,
emphasizing the need for collaborative, responsible approaches that balance
technological innovation with ecological sustainability.
Keywords: Artificial intelligence, Biodiversity conservation, Climate change, Data analytics, Environmental decision-making, Ethical AI, Environmental monitoring, Multimodal data, Sustainability, Smart agriculture.
