Eager and Lazy Learning Methods in the Context of Hydrologic Forecasting

Abstract

Computational intelligence techniques are becoming popular in hydrologic forecasting. Primarily these are eager learning methods. Lazy (instance-based) learning (IBL) has received relatively little attention, and the present paper explores the applicability of these methods. Their performance is compared with that of neural networks, M5 model trees, regression trees. A flow forecasting problem was solved along with the five benchmark problems. Results showed that one of the IBL methods, the locally weighted regression, especially if used with the Gaussian kernel function, often is more accurate than the eager learning methods.

DOI: 10.1109/IJCNN.2006.247163

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Cite this paper

@article{Solomatine2006EagerAL, title={Eager and Lazy Learning Methods in the Context of Hydrologic Forecasting}, author={Dimitri P. Solomatine and Mahesh Maskey and Durga L. Shrestha}, journal={The 2006 IEEE International Joint Conference on Neural Network Proceedings}, year={2006}, pages={4847-4853} }