[HTML][HTML] Deformation mechanism-assisted deep learning architecture for predicting step-like displacement of reservoir landslide
Reservoir landslides in the Three Gorges Reservoir, China, exhibit prolonged slow motion
and the potential for catastrophic events due to fluctuations in reservoir levels and intense …
and the potential for catastrophic events due to fluctuations in reservoir levels and intense …
Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting
L Liu, X Liang, YP Xu, Y Guo, QJ Wang, H Gu - Journal of Hydrology, 2024 - Elsevier
Abstract Reliable Tropical Cyclone (TC) rainfall and flood forecasts play an important role in
disaster prevention and mitigation. Numerous studies have demonstrated the promising …
disaster prevention and mitigation. Numerous studies have demonstrated the promising …
[HTML][HTML] Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning
Abstract Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are
revolutionizing hydrology, driving significant advancements in water resource management …
revolutionizing hydrology, driving significant advancements in water resource management …
Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application
Pre-trained models like FourCastNet, Pangu and GraphCast have gained popularity in the
meteorological field. In hydrology, data-driven rainfall-runoff models based on long short …
meteorological field. In hydrology, data-driven rainfall-runoff models based on long short …
Does grou** watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?
Predicting accurate streamflow for data-limited regions and poorly gauged watersheds
remains a global challenge. The complex calibration of physically based models (PBMs) …
remains a global challenge. The complex calibration of physically based models (PBMs) …
Mixture of experts leveraging informer and LSTM variants for enhanced daily streamflow forecasting
Z Rong, W Sun, Y **e, Z Huang, X Chen - Journal of Hydrology, 2025 - Elsevier
Streamflow forecasting is of paramount importance for water resources management and
flood prevention. Machine learning, particularly deep learning, has had significant success …
flood prevention. Machine learning, particularly deep learning, has had significant success …
[HTML][HTML] Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data
Floods, increasingly exacerbated by climate change, are among the most destructive natural
disasters globally, necessitating advancements in long-term forecasting to improve risk …
disasters globally, necessitating advancements in long-term forecasting to improve risk …
[HTML][HTML] ConvFormer-KDE: A Long-Term Point–Interval Prediction Framework for PM2. 5 Based on Multi-Source Spatial and Temporal Data
S Lin, Y Zhang, X Fei, X Liu, Q Mei - Toxics, 2024 - pmc.ncbi.nlm.nih.gov
Accurate long-term PM2. 5 prediction is crucial for environmental management and public
health. However, previous studies have mainly focused on short-term air quality point …
health. However, previous studies have mainly focused on short-term air quality point …
Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso
Y Song, J Zhang - Water Science & Technology, 2024 - iwaponline.com
With the widespread application of machine learning in various fields, enhancing its
accuracy in hydrological forecasting has become a focal point of interest for hydrologists …
accuracy in hydrological forecasting has become a focal point of interest for hydrologists …