The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts.

Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management.

Volume Highlights Include:

Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics
Advances in modeling hydrological systems
Different data analysis methods and models for forecasting water resources
New areas of knowledge discovery and optimization based on using machine learning techniques
Case studies from North America, South America, the Caribbean, Europe, and Asia

The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

About the Author

Gerald A. Corzo Perez, IHE Delft Institute for Water Education, The Netherlands

Dimitri P. Solomatine, IHE Delft Institute for Water Education, and Delft University of Technology, The Netherlands, and Water Problems Institute of the Russian Academy of Sciences, Moscow, Russia

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