Chinese researchers have proposed a novel hybrid deep-learning model to address streamflow forecasting for water catchment areas at a global scale, with a view to improving flood prediction, according to a recent research article published in the journal The Innovation.
Streamflow and flood forecasting remains one of the long-standing challenges in hydrology. Traditional physically based models are hampered by sparse parameters and complex calibration procedures particularly in ungauged catchments.
More than 95 percent of small and medium-sized water catchments in the world lack monitoring data, according to the Chinese Academy of Sciences (CAS).
Researchers from the Institute of Mountain Hazards and Environment of the CAS used the datasets of more than 2,000 catchments around the world to conduct model training in order to cope with streamflow forecasting at a global scale for all gauged and ungauged catchments.
The distribution of these catchments was significantly different, ensuring the diversity of data.
The results show that the forecasting accuracy of the model was higher than traditional hydrological models and other AI models.
The study demonstrated the potential of deep-learning methods to overcome the lack of hydrologic data and deficiencies in physical model structure and parameterization, the research article noted.
Met Gala 2024: Rebecca Hall embraces Garden of Time theme with semi
NEV makers encouraged to speed up overseas development
China Hosts Forum on Global Human Rights Governance
Xi Story: China's Youth Stand at Forefront of Innovation
Soccer fans now have a day to celebrate the world's most popular sport
Xi Extends Condolences to Indian President, PM over Deadly Train Accident
Creations of Laurèl staged at China Fashion Week
Elle Fanning puts on a leggy display as she changes into a silver sequinned gown with daring thigh
Citizens in Yinchang enjoyed outdoor skiing during the Spring Festival holiday