Hydroinformatics and Applications of Artificial Intelligence and Machine Learning in Water-Related Problems

Book Chapter (2024)
Author(s)

Gerald A. Corzo Perez (IHE Delft Institute for Water Education)

Dmitri Solomatine (TU Delft - Water Resources, IHE Delft Institute for Water Education, Water Problems Institute of Russian Academy of Sciences)

Research Group
Water Resources
DOI related publication
https://doi.org/10.1002/9781119639268.ch1
More Info
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Publication Year
2024
Language
English
Research Group
Water Resources
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Pages (from-to)
1-38
ISBN (print)
9781119639312
ISBN (electronic)
9781119639268
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Abstract

In recent years, there has been a surge of interest in machine learning (ML) and artificial intelligence (AI) due to the effectiveness of deep learning algorithms and the increasing availability of large data sets. This chapter provides a brief overview of the applications of AI and ML techniques in hydroinformatics, a field that deals with advanced information technology, data analytics, and modeling for aquatic environment management. Data-driven models are becoming more common in water management as they can reveal hidden patterns in data and offer improved accuracy in certain situations. This chapter highlights the importance of spatiotemporal data analysis, pattern recognition, and optimization approaches in water resources management under uncertainty. It does not offer a comprehensive review of all methods but rather focuses on selected ML techniques widely used in water-related problems. Additionally, the chapter discusses the challenges associated with using ML models, such as black-box criticisms, and the potential of hybrid models that combine the strengths of ML and physically based process models for more robust solutions in hydroinformatics.

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