Deadline, 31 July 2024.


Dear Colleagues,

In the last years, applications based on machine learning (ML) have been widely used to solve problems in different scientific areas. Within the current ML algorithms, support vector machines, Bayesian networks, and artificial neural networks, among others, can be mentioned.

Currently, there are many monitoring instruments/stations that allow a daily collection of hydrological data. Different ML-based models can be fed with these data to study/model the following: dam/water supply management, extreme events, natural/anthropogenic changes in lakes, transport of pollutants, drinking water quality, landslides induced by rain, etc.

The objective of this Special Issue on “Application of Machine Learning in Hydrologic Sciences” is to present current research on the aforementioned problems (but not limited exclusively to them) using machine learning.

We invite all researchers, working in hydrological sciences and ML, to submit research or review articles that demonstrate the significant potential of machine learning in this field.

Dr. Gonzalo Astray
Dr. Diego Fernández-Nóvoa
Guest Editors


  • hydrology
  • water cycle
  • water–soil–atmosphere
  • machine learning
  • big data
  • monitoring/modelling/prediction/optimization/management
  • water flow/quality/supply/energy
  • risk/hazard assessment
  • multidisciplinary water research