A systematic review published in the journal Environments assesses the application of artificial intelligence in monitoring transitional water ecosystems, revealing significant insights from 96 peer-reviewed studies. This comprehensive analysis highlights the growing importance of machine learning tools for understanding complex environments such as estuaries and coastal wetlands, where traditional ecological models often fall short.
AI's capability to manage large sets of diverse data enables it to identify patterns in these nonlinear ecosystems effectively. Dominant techniques, including Random Forest and Support Vector Machines, excel in tasks like predicting water quality and locating pollution sources. Notably, regression-based methods make up over 44 percent of the reviewed methodologies, focusing on estimating environmental variables such as nutrient and chlorophyll levels.
Moreover, water quality monitoring has emerged as a crucial application of AI, facilitating rapid response to pollution incidents. Beyond this area, the technology also plays a role in biodiversity assessment and habitat mapping, often utilizing data from remote sensing and in situ sensors. The rise of deep learning technologies since 2020 is noted as a transformative development in the field, enhancing the capacity for ecosystem forecasting and comprehensive analysis.