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Yanan Duan

Soil moisture is crucial for agriculture and hydrology, but its accurate prediction is challenging due to inadequate representation of various complex land surface processes and meteorological influences. In this research, we employ the Long Short-Term Memory (LSTM) framework, a specific architecture of deep learning networks that is effective in processing time series data, for predicting soil moisture. We have developed the Next Generation Interactive Soil Moisture Forecasting System to advance skillful soil moisture predictions at sub-seasonal timescales by leveraging advanced analytics and deep learning, with LSTM at its core. We combined the state-of-the-art climate model's (Community Earth System Model Version...
Categories: Publication; Types: Citation
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