HydroNetwork is a user-friendly Python library designed to empower hydrologists in leveraging deep learning techniques for modeling complex hydrological processes such as precipitation, runoff, evaporation, and groundwater flow. By providing comprehensive tools for data processing, building models, and visualizing results, HydroNetwork makes deep learning accessible even for users with limited programming or machine learning experience. It seamlessly integrates advanced modeling techniques and traditional hydrological methods to reduce the cognitive load for researchers.
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Deep Learning Model Interface 😏
- Build, train, and evaluate deep learning models for hydrological time series forecasting with ease.
- Supports widely used architectures: LSTM, GRU, CNN, and Hybrid Models.
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Flexible Data Processing Tools 🧐
- Preprocess raw hydrological data with features like handling missing data, normalization, and time series segmentation.
- Automate feature selection and extraction for better model performance.
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Visualization Tools 🥳
- Visualize input hydrological time series data for exploratory data analysis.
- Evaluate and graphically compare the performance of trained models using loss curves, hydrographs, or model efficiency metrics.
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Model Integration 🤗
- Combine multiple deep learning models and traditional hydrological models for improved predictions.
Comprehensive API documentation is available on the Research Progress module here: HydroNetwork Documentation
HydroNetwork is open source and hosted on GitHub. Contributions are welcome! Please star the repository or raise issues for bug reports or enhancements. If you find HydroNetwork helpful, feel free to buy me a coffee to support the project.
HydroNetwork is licensed under the MIT License.