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.
Related articles:
Related suggestion:
Iraqi PM vows to diversify national economyChina expresses grave concerns over Japan's planned export controlsMaple Leafs star Auston Matthews to miss Game 6 of firstIsraeli troops withdrawn from Gaza to prepare Rafah operation: Defense ministerDeath toll from Kenya floods reaches 179, evacuations underwayChinese diplomat refutes trade restrictions, calls for common developmentSo you've lost weight using Wegovy. Does that mean you can stop taking it?Swiatek returns to Madrid Open final by beating Keys in straight setsUCLA cancels all classes after overnight violence on campusLandmark Google antitrust case ready to conclude
3.1752s , 6577.1171875 kb
Copyright © 2024 Powered by Researchers develop deep ,Stellar Scope news portal