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. |
More misery as the UK's tax burden is set to hit an 80DEAR JANE: My wife has ditched her razor for goodCelebrity bodyguardMy £142k home is unsellable after builders made a MAJOR blunder and then went bust... I'm trappedMany Florida women can't get abortions past 6 weeks. Where else can they go?Urgent search for P&O cruise ship passenger feared to have fallen overboard near Sydney HarbourWhat to stream: 'Iron Claw,' 'Pretty Little Liars,' Ryan CastroShohei Ohtani homers twice as Dodgers sweep Braves with 5Rublev overcomes fever and praises doctors after winning Madrid Open for the 1st timeFrank Stella, renowned American artist, dies at 87