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[HTML][HTML] Improving global soil moisture prediction through cluster-averaged sampling strategy
Q Li, Q **ao, C Zhang, J Zhu, X Chen, Y Yan, P Liu… - Geoderma, 2024 - Elsevier
Understanding and predicting global soil moisture (SM) is crucial for water resource
management and agricultural production. While deep learning methods (DL) have shown …
management and agricultural production. While deep learning methods (DL) have shown …
Soil temperature prediction based on explainable artificial intelligence and LSTM
Q Geng, L Wang, Q Li - Frontiers in Environmental Science, 2024 - frontiersin.org
Soil temperature is a key parameter in many disciplines, and its research has important
practical significance. In recent years, the prediction of soil temperature by deep learning …
practical significance. In recent years, the prediction of soil temperature by deep learning …
[HTML][HTML] Enhancing hydrological variable prediction through multitask LSTM models
Y Yan, G Li, Q Li, J Zhu - Water, 2024 - mdpi.com
Deep learning models possess the capacity to accurately forecast various hydrological
variables, encompassing flow, temperature, and runoff, notably leveraging Long Short-Term …
variables, encompassing flow, temperature, and runoff, notably leveraging Long Short-Term …
[HTML][HTML] Enhancing soil moisture forecasting accuracy with REDF-LSTM: Integrating residual en-decoding and feature attention mechanisms
X Li, Z Zhang, Q Li, J Zhu - Water, 2024 - mdpi.com
This study introduces an innovative deep learning model, Residual-EnDecode-Feedforward
Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the …
Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the …
Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification
Soil moisture (SM) is crucial in global climate change research, facilitating in the
understanding of hydrological cycles. Recently, advances in the use of deep learning (DL) in …
understanding of hydrological cycles. Recently, advances in the use of deep learning (DL) in …
Enhancing data-driven soil moisture modeling with physically-guided LSTM networks
Q Geng, S Yan, Q Li, C Zhang - Frontiers in Forests and Global …, 2024 - frontiersin.org
In recent years, deep learning methods have shown significant potential in soil moisture
modeling. However, a prominent limitation of deep learning approaches has been the …
modeling. However, a prominent limitation of deep learning approaches has been the …
[HTML][HTML] Integrating Convolutional Attention and Encoder–Decoder Long Short-Term Memory for Enhanced Soil Moisture Prediction
J Han, J Hong, X Chen, J Wang, J Zhu, X Li, Y Yan… - Water, 2024 - mdpi.com
Soil moisture is recognized as a crucial variable in land–atmosphere interactions. This study
introduces the Convolutional Attention Encoder–Decoder Long Short-Term Memory …
introduces the Convolutional Attention Encoder–Decoder Long Short-Term Memory …
Machine Learning Methods for Weather Forecasting: A Survey
Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved
significantly over the years. Comprehensive surveys play a crucial role in synthesizing …
significantly over the years. Comprehensive surveys play a crucial role in synthesizing …