Water Hyacinth Invasion and Management in a Tropical Hydroelectric Reservoir: Insights from Random Forest and SVM Classification

The rapid proliferation of water hyacinth (Eichhornia crassipes) in newly formed reservoirs poses a significant threat to aquatic ecosystems and hydroelectric operations. The objective of this study was to map and monitor the spatio-temporal distribution of water hyacinth in the Hidroituango reservoir in Colombia from 2018 to 2023, using Sentinel-2 satellite imagery and machine learning algorithms. The Random Forest (RF) and Support Vector Machine (SVM) algorithms were employed for image classification, and their performance was evaluated using various accuracy metrics. The results revealed that both algorithms effectively detected and mapped water hyacinth infestations, with RF demonstrating greater stability in capturing long-term trends and SVM exhibiting higher sensitivity to rapid changes in coverage. The study also highlighted the impact of the COVID-19 pandemic on control efforts, leading to a temporary increase in infestation. The findings underscore the importance of continuous monitoring and adaptive management strategies to mitigate the ecological and economic impacts of water hyacinth in the Hidroituango reservoir and similar environments.