Researchers successfully implement distributed hydrological modeling in deep neural networks
In recent years, with the accumulation of Earth’s big data, data-driven methods represented by deep learning have surpassed traditional hydrological models in simulation accuracy. Still, the black-box...
Researchers highlight urgent need for improved modeling of evapotranspiration sensitivity to vegetation change
Concerns regarding the interactions between vegetation and climate extend over a century and endure in the present era. Acting as biotic pumps, vegetation transports water from the land and releases m...
Researchers use machine learning to guide global agricultural ammonia emissions reduction
Ammonia (NH3) is a major air pollutant and a key driver of smog formation. Agricultural emissions are the leading source of NH3, accounting for approximately 51-60% of global anthropogenic NH3 em...
