This chapter will address the use of different data fusion techniques for integrating or combining hydrological models. Different approaches will be demonstrated using flow forecasting models from the River Ouse catchment in the UK for a lead time of 6 hours. These approaches include simple averaging, neural networks, fuzzy logic, M5 model trees and instance-based learning. The results show that the data fusion approaches produce better performing models compared to the individual models on their own. The potential of this approach is demonstrated yet remains largely unexplored in real-time hydrological forecasting.
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See, L. (2008). Data Fusion Methods for Integrating Data-driven Hydrological Models. In: Cai, X., Yeh, T.C.J. (eds) Quantitative Information Fusion for Hydrological Sciences. Studies in Computational Intelligence, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75384-1_1
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