🙋♀️ 1. Forecasting daily and hourly dam inflow using various Deep Learning approaches in neuralhydrology
🌈 2. Forecaseting daily and hourly dam downstream streaflow and waterlevel using various Deep Learning approaches in neuralhydrology
👩💻 3. Application of MC-LSTM to
🍿 4. Application of Differentiable Modeling to analyze streamflow using Muskingum-Cunge
🧙 5. Data Assimilation for Real-time AI Tool Operation
Forked from mhpi/dMC-Juniata-hydroDL2
A novel differentiable modeling framework to perform routing and to learn a “parameterization scheme” (a systematic way of inferring parameters from more rudimentary information) for routing flows …
Python
Forked from neuralhydrology/neuralhydrology
Python library to train neural networks with a strong focus on hydrological applications.
Python
Forked from HIPS/autograd
Efficiently computes derivatives of NumPy code.
Python
Time-series forecasting with 1D Conv model, RNN (LSTM) model and Transformer model. Comparison of long-term and short-term forecasts using synthetic timeseries. Sequence-to-sequence formulation.
A python library for time-series smoothing and outlier detection in a vectorized way.
Free MLOps course from DataTalks.Club
Python library to train neural networks with a strong focus on hydrological applications.
Machine Learning Engineering with MLflow, published by Packt
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