Earth Science Frontiers ›› 2023, Vol. 30 ›› Issue (4): 451-469.DOI: 10.13745/j.esf.sf.2023.2.52

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Modeling of hydrological processes in cryospheric watersheds based on machine learning

SONG Xuanyu1,3(), XU Min1,2,*(), KANG Shichang1,2, SUN Liping4   

  1. 1. State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Science, Lanzhou 730000, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Mathematics, Lanzhou Jiaotong University, Lanzhou 730070, China
    4. School of Science, Northeastern University, Shenyang 110819, China
  • Received:2023-01-28 Revised:2023-02-20 Online:2023-07-25 Published:2023-07-07


Machine learning models are widely used in hydrological research for their high predictive accuracy, however, their application in high-altitude cryospheric watersheds is seldom mentioned. In this study, machine learning models for two typical cryospheres, Yarkant and Shule river basins, were developed using BP neural network (BP), GRNN neural network (GRNN), RBF neural network (RBF), support vector regression (SVR), genetic optimization BP neural network (GA-BP) and double-layer long-term and short-term memory neural network (LSTM) algorithms, and model performance was evaluated using evaluation indexes NSE, RMSE and R and runoff frequency curves. The double-layer LSTM model performed much better than and similar to other models for the Yarkant and Shule River Basins, respectively; and overall the double-layer LSTM algorithm was more suitable for modeling hydrological processes in cryosphere basins. The loss function was used to evaluate the model parameterization scheme. It was found that the performance of the LSTM models was mainly affected by the optimizer, followed by the learning attenuation rate and initial learning rate for the Yarkant River Basin, and by the initial learning rate for the Shule River Basin. Model testing under abrupt changes in runoff suggested that climatic factors could have different hydrological impacts, meanwhile, precipitation and heavy precipitation R95p had the greatest impacts on the hydrological process in the study area, followed by temperature, during the entire study period.

Key words: climate change, machine learning, cryospheric watersheds, hydrological process simulation, parameterization

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