Assessing Riparian Wetland Health Using Machine Learning: Analyzing Vegetation and Hydrological Correlations

Riparian wetlands are vital ecosystems that provide essential services, including biodiversity support, water quality improvement, and flood mitigation. Despite their importance, these ecosystems face numerous threats, such as habitat loss, pollution, and climate change. Monitoring the health of riparian wetlands is complex due to the intricate interactions between vegetation and hydrological processes. This study addresses these challenges by using machine learning techniques to analyze the relationships between key ecological indicators, including hydrophyte cover, species richness, and hydrological features like water cover and litter accumulation. Using data from the BLM Natl AIM Riparian and Wetland Indicators Hub, various machine learning models—Random Forest, Gradient Boosting, Extra Trees, and Ridge Regression—were applied to predict wetland health outcomes. The results revealed strong correlations between hydrophyte cover and composite wetland value, as well as between species richness and native vegetation cover, underscoring the importance of these variables in maintaining ecosystem integrity. The models effectively captured the complex relationships within riparian wetlands, providing accurate predictions of ecological health. These findings highlight the potential of integrating vegetation and hydrological data with advanced analytical techniques to enhance wetland conservation and management. This research contributes to a better understanding of riparian wetland dynamics and offers valuable insights for developing targeted conservation strategies to preserve these crucial ecosystems.