Researchers develop deep
Time:2024-05-08 22:53:40 Source:businessViews(143)
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.
You may also like
- Met Gala 2024: Iris Law commands attention in a racy sheer black Versace dress and wet
- New defense minister talks with Russian counterpart
- Bare mountains turn green again through ecological restoration in SW China's Sichuan
- China's space station looking forward to participation of foreign astronauts
- China's telecoms industry expands steadily in Q1
- New productive forces key to growth, Xi says
- Feature: Return of Chinese tourists contributes to Egypt's tourism rebound
- Xi pays visit to grassroots officials and residents in Tianjin ahead of Spring Festival
- Recreational marijuana backers try to overcome rocky history in South Dakota