Machine learning for hydrologic sciences: An introductory overview

The hydrologic community has experienced a surge in interest in machine learning in recent years. This interest is primarily driven by rapidly growing hydrologic data repositories, as well as success of machine learning in various academic and commercial applications, now possible due to increasing accessibility to enabling hardware and software. This overview is intended for readers new to the field of machine learning. It provides a non-technical introduction, placed within a historical context, to commonly used machine learning algorithms and deep learning architectures. Applications in hydrologic sciences are summarized next, with a focus on recent studies. They include the detection of patterns and events such as land use change, approximation of hydrologic variables and processes such as rainfall-runoff modeling, and mining relationships among variables for identifying controlling factors. The use of machine learning is also discussed in the context of integrated with process-based modeling for parameterization, surrogate modeling, and bias correction. Finally, the article highlights challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications.

Automated summary

In recent years, the hydrologic community has shown a growing interest in machine learning, driven by the expansion of hydrologic data repositories and the success of machine learning in academic and commercial applications. This overview introduces commonly used machine learning algorithms and deep learning architectures in a non-technical manner, with a focus on applications in hydrologic sciences. The challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications are also highlighted.