NeuralHydrology - A Python library for Deep Learning research in hydrology
NeuralHydrology - A Python library for Deep Learning research in hydrology
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NeuralHydrology - 用于水文学深度学习研究的 Python 库
DOI：
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发表时间：
2022
影响因子：
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通讯作者：
D. Klotz
中科院分区：
文献类型：
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作者：
Frederik Kratzert;M. Gauch;G. Nearing;D. Klotz
自古以来，人类就努力描述与水有关的环境过程（Angelakis et al., 2012; Biswas, 1970）。在这段历史中，水文学家建立了各种基于过程的预测模型，模拟从土壤湿度到水流生成的过程（Loague（2010）收集了一些历史参考资料）。最近，深度学习模型已经成为这些传统建模方法的极其强大和更准确的替代品（Gauch等人，2021；Klotz等人，2021;Kratzert, Klotz， Shalev等人，2019;Kratzert, Klotz， Herrnegger等人，2019；Lees等人，2021）。对于水文学家来说，接受这种新的数据驱动范式是具有挑战性的（Beven, 2020; nearby et al., 2021）：不仅模型在概念上不同，而且它们也是用不同的策略和工具集构建、优化和评估的。
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process-based prediction models that simulate processes from soil moisture to streamflow generation (a collection of historical references can be found in Loague (2010)). More recently, Deep Learning models have emerged as extremely powerful and more accurate alternatives to these traditional modeling approaches (Gauch et al., 2021; Klotz et al., 2021; Kratzert, Klotz, Shalev, et al., 2019; Kratzert, Klotz, Herrnegger, et al., 2019; Lees et al., 2021). For hydrologists, embracing this new data-driven paradigm is challenging (Beven, 2020; Nearing et al., 2021): not only are the models conceptually different, but they are also built, optimized, and evaluated with different strategies and toolsets.