Speaker
Mahmoud Saleh
(Channel Maintenance Research Institute (CMRI), National Water Research Center (NWRC))
Description
Machine learning algorithms represent a transformative advance in monitoring water hyacinth and aquatic vegetation using satellite imagery, particularly in Egypt and Africa, where invasive aquatic weeds pose significant ecological and economic challenges. Recent studies illustrate that RF, SVM, and CNN methods applied to Sentinel-2 and Landsat data improve detection accuracy, temporal monitoring, and assessment of management interventions. Continued development integrating multi-sensor data, explainable approaches, and enhanced training datasets will further boost capability, supporting sustainable water resource management in vulnerable regions.
