About this Research Topic
This Article Collection welcomes submissions that focus on the application of machine learning approaches to better predict and understand water resources behaviors. We envision papers that have, at the same time, a component of machine learning algorithms (e.g. applications or developments in neural networks approaches in their various forms, non-parametric classification or regression on large datasets, non-parametric spatial processes modeling) as well as a component of water resources modeling (from global to local scale, encompassing e.g. atmospheric processes, surface hydrology, or subsurface flow and transport processes).
We particularly encourage in the following domains, although other topics might be of interest as well:
- Improved predictions of hydrological, hydrogeological or hydroclimatological variables
- New ways of using machine learning approaches to unravel hydrological processes (opening the black box)
- Application of machine learning in fields where it was not considered before
- Approaches where statistical learning can be seen as an advantageous alternative to physical description of a hydrological system
- Ways to address scale dependencies between punctual and areal measurements of the water cycle
Keywords: remote sensing, machine learning, water, water resources, big data, artificial intelligence, water cycle, hydrology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.