Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

• Published in: Advances in atmospheric sciences Vol. 41; no. 7; pp. 1326 - 1341
• Main Authors: Li, Lu, Dai, Yongjiu, Wei, Zhongwang, Shangguan, Wei, Wei, Nan, Zhang, Yonggen, Li, Qingliang, Li, Xian-Xiang
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.07.2024; Springer Nature B.V; School of Atmospheric Sciences,Sun Yat-sen University,Guangzhou 510275,China; Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Guangzhou 510275,China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies,Guangzhou 510275,China%Institute of Surface-Earth System Science,School of Earth System Science,Tianjin University,Tianjin 300072,China%College of Computer Science and Technology,Changchun Normal University,Changchun 130123,China
• Subjects: AI Applications in Atmospheric and Oceanic Science: Pioneering the Future; Atmospheric Sciences; Deep learning; Drought; Earth and Environmental Science; Earth Sciences; Forecasting models; Geophysics/Geodesy; Hydrologic processes; Long short-term memory; Meteorology; Moisture content; Original Paper; Physics; Predictions; Soil; Soil conditions; Soil moisture; soil moisture forecasting; hybrid model; deep learning; 注意力机制; 卷积长短期记忆模型; attention mechanism; 深度学习; 土壤湿度预报; ConvLSTM; 混合模型

Accurate soil moisture (SM) prediction is critical for understanding hydrological processes. Physics-based (PB) models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes. In addition to PB models, deep lear...