Journal of Hydroinformatics Vol 10 No 1 pp 3–22 © IWA Publishing 2008 doi:10.2166/hydro.2008.015
Data-driven modelling: some past experiences and new approaches
Dimitri P. Solomatine and Avi Ostfeld
UNESCO-IHE Institute for Water Education, PO Box 3015, 2601 DA, Delft, The Netherlands d.solomatine@unesco-ihe.org
Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel
ABSTRACT
Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system's physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.
Keywords: computational intelligence; data-driven modelling; neural networks; river basin management; simulation modelling
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