Demesew A. Mhiret

Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia

Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters and offers a transferable solution for strengthening freshwater monitoring in data-scarce regions.

Sustainable Land Management Practices Enhance Soil Microbial Biomass Carbon in Global Croplands

Machine Learning-Informed Hydrological Response Time Modeling in Tropical Watersheds

Flood occurrence in tropical regions is intensifying due to climate variability and land-use change, increasing the need for reliable flood response time estimation. Accurate prediction of flood lag time (TL)—the interval between the centroid of excess rainfall and peak runoff—is critical for flood early warning and wa...