Abstract

NeuralHydrology — A Python library for Deep Learning research in hydrology

Highlights

  • Since ancient times humans have strived to describe environmental processes related to water (Angelakis et al, 2012; Biswas, 1970). Throughout this history, hydrologists built various process-based prediction models that simulate processes from soil moisture to streamflow generation (a collection of historical references can be found in Loague (2010))

  • NeuralHydrology is a Python library based on PyTorch (Paszke et al, 2019) that is designed to build, apply, and experiment with Deep Learning models with a strong focus on hydrological applications

  • Designed for our internal research needs, the library was generalized and open-sourced to allow anyone to experiment with Deep Learning models as as possible: pre-built models and data loaders allow for quick experiments, yet the framework is extensible to new models, data sets, loss functions, or metrics to suit more advanced use-cases

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Summary

Introduction

Since ancient times humans have strived to describe environmental processes related to water (Angelakis et al, 2012; Biswas, 1970). NeuralHydrology is a Python library based on PyTorch (Paszke et al, 2019) that is designed to build, apply, and experiment with Deep Learning models with a strong focus on hydrological applications.

Results
Conclusion

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