Machine Learning Algorithms for Hydraulic Engineering

Dear Colleagues,

As is well known, machine learning (ML) is one of the main branches of artificial intelligence (AI). Its primary objective is to use computational methods to extract information from data. Machine learning has a wide spectrum of practical applications. After the first applications concerning topics such as recognition of manual writing, detection of objects in image processing, voice recognition, medical diagnoses, DNA classification, search engines, and stock market analysis, in recent years machine learning algorithms have been increasingly used in environmental sciences due to their high capability for modelling non-linear phenomena. In particular, these algorithms are already widely used in weather and climate forecasts, as well as in the analysis and modelling of hydrological, ecological, and oceanographic data. In any case, they represent a very powerful tool for dealing with a wide variety of issues in hydraulic engineering.

This Special Issue of Applied Sciences entitled “Machine Learning Algorithms for Hydraulic Engineering” aims to cover recent advances in machine-learning-based modelling for addressing the following topics:

• River flow forecasting at different time scales;
• Water balance models for water resources investigations;
• Global estimates of the land–atmosphere water flux;
• Water demand prediction;
• Water quality characterization;
• Performance forecasting of hydraulic devices (e.g. Valve, CSO, etc.);
• Support to experimental activities;
• Long-term predictions of significant wave height;
• Wave energy extraction systems;

Additional different topics can be proposed by the authors.

Dr. Francesco Granata
Prof. Rudy Gargano
Guest Editors

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