AI-Driven Environmental Monitoring: From Data Streams to Ecosystem Insights

Abstract

Abstract— The rapid rate of environmental degradation, climatic change and loss of biodiversity require strong and intelligent monitoring systems. Standard environmental surveillance solutions have been known to fail when it comes to scale, real-time or multi-dimensional analysis. The current paper describes the transformative potential of the implementation of Artificial Intelligence (AI) to environmental monitoring by considering how the AI has the potential to provide meaningful insight on the ecosystem through processing massive, dynamic data streams. We survey modern methods, machine learning (ML), deep learning, and data fusion methods connected to sensor approaches, and explain their use in air quality sensing, water pollution sensors and monitoring, forest solutions, and climate prediction. The article introduces a converged approach using edge sensing and cloud analytics and long-term future prediction AI models. Different examples of AI implementation in real settings in the environment indicate great enhancement in accuracy and response time as well as predictive effectiveness. This study highlights the potential of AI to shift environmental surveillance into the active management of ecosystems based on knowledge gained by active data gathering rather than passive.