Pinned Repositories
Dataset4HydroPML
Dataset4LandslideNets
https://doi.org/10.5281/zenodo.10294997
DisasterNets
https://arxiv.org/abs/2306.09815
FloodCast
Large-scale flood modeling and forecasting with FloodCast (https://doi.org/10.1016/j.watres.2024.122162)
FloodCastBench
hydropml.github.io
Offitial website for HydroPML
HydroPML_Flood
The operational rainfall-runoff-inundation forecasting system based on HydroPML
Landslidecast
HydroPML for landslide dynamic process modeling and forecast (https://doi.org/10.1029/2023EA003417)
PaML
Physics-aware ML (PaML) aims to take the best from both physics-based modeling and state-of-the-art ML models to better solve scientific problems (https://arxiv.org/abs/2310.05227)
PaML_PeML
Physics-embedded Machine Learning (PeML) is achevied by embedding physics in the model frameworks or modules.
HydroPML's Repositories
HydroPML/DisasterNets
https://arxiv.org/abs/2306.09815
HydroPML/FloodCast
Large-scale flood modeling and forecasting with FloodCast (https://doi.org/10.1016/j.watres.2024.122162)
HydroPML/Dataset4LandslideNets
https://doi.org/10.5281/zenodo.10294997
HydroPML/Landslidecast
HydroPML for landslide dynamic process modeling and forecast (https://doi.org/10.1029/2023EA003417)
HydroPML/PaML
Physics-aware ML (PaML) aims to take the best from both physics-based modeling and state-of-the-art ML models to better solve scientific problems (https://arxiv.org/abs/2310.05227)
HydroPML/Dataset4HydroPML
HydroPML/HydroPML_Flood
The operational rainfall-runoff-inundation forecasting system based on HydroPML
HydroPML/PaML_PeML
Physics-embedded Machine Learning (PeML) is achevied by embedding physics in the model frameworks or modules.
HydroPML/FloodCastBench
HydroPML/hydropml.github.io
Offitial website for HydroPML
HydroPML/HydroPML_Hydrodynamic
Hydrodynamic models are mathematical models that attempt to replicate fluid motion and typically require solving computationally. These models simulate water movement by solving equations formulated by applying laws of physics. Hydrodynamic models can realize the simulation of hydrological process in seconds, hours to days time scale.
HydroPML/HydroPML_HydrodynamicBench
Large-scale data sets and benchmarks for hydrodynamic modeling based on physics-aware machine leaning
HydroPML/HydroPML_Rainfall_runoff
Rainfall-runoff Forecast Meets Physics-aware Machine Learning
HydroPML/PaML_PaHL
Physics-aware Hybrid Learning (PaHL) directly combines pure physics-based models, such as numerical methods, climate, land, hydrology and earth system models, with ML models. According to the hybrid way, hybrid learning can be divided into serial way, parallel way, and complex way.
HydroPML/PaML_PDgML
Physical Data-guided Machine Learning (PDgML) is a supervised DL model that statistically learns the known or unknown physics of a desired phenomenon by extracting features or attributes from raw training data.
HydroPML/PaML_PiML
PiML is a widely used approaches to incorporate physical constraints, which can be trained from additional information obtained by enforcing the physical laws (for example, designing loss functions (regularization))