Enhancing environmental decision-making: a systematic review of data analytics applications in monitoring and management.

Escalating environmental challenges necessitate paramount decision-making to safeguard ecosystems and resources. However, the burgeoning volume and intricate nature of environmental data often present a formidable challenge in gleaning actionable insights. In this context, integrating data analytics tools within environmental monitoring and management frameworks offers a compelling avenue for progress. These tools facilitate efficient data processing, uncover hidden patterns, and enable predictive modelling, leading to more informed decisions. Despite growing research, a comprehensive understanding of specific data analytics applications, methodologies, and demonstrably effective implementations remains elusive. This systematic review aimed to address this gap. Following PRISMA guidelines, a meticulous search across five databases was conducted using predefined inclusion/exclusion criteria. Rigorous data extraction captured salient study characteristics, methodologies, data analysis techniques, key findings, and acknowledged limitations. The review revealed that data analytics offers a powerful toolkit for environmental management, transforming decision-making across all stages. Big data and advanced techniques enable proactive strategies through earlier issue detection and improved predictive models. However, maximising this potential requires a multifaceted approach, including standardised data collection, data literacy, ethical frameworks, and stakeholder engagement. Highlights: Data analytics as a powerful tool The study underscored that data analytics is a crucial tool for addressing environmental challenges, offering transformative benefits at all stages of environmental decision-making. Enhanced environmental management The study also revealed that advancements in big data and machine learning empower earlier detection of environmental concerns, facilitate accurate prediction, and foster proactive management strategies. Multifaceted approach for success The major implication of the study is that maximising the potential of data analytics requires a multifaceted approach that addresses data quality, human and ethical considerations, interoperability, stakeholder engagement, and cultural factors. [ABSTRACT FROM AUTHOR]