Abstract
Data-driven modelling is the area of hydroinformatics undergoing fast development. This chapter reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods. A brief overview of the main methods – neural networks, fuzzy rule-based systems and genetic algorithms, and their combination via committee approaches – is provided along with hydrological examples and references to the rest of the book.
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Solomatine, D., See, L., Abrahart, R. (2009). Data-Driven Modelling: Concepts, Approaches and Experiences. In: Abrahart, R.J., See, L.M., Solomatine, D.P. (eds) Practical Hydroinformatics. Water Science and Technology Library, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79881-1_2
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DOI: https://doi.org/10.1007/978-3-540-79881-1_2
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