Instance-based learning compared to other data-driven methods in hydrological forecasting
2008
Solomatine, Dimitri P. | Māske, Maheśa | Śreshṭha, Durgālāla
Data-driven techniques based on machine learning algorithms are becoming popular in hydrological modelling, in particular for forecasting. Artificial neural networks (ANNs) are often the first choice. The so-called instance-based learning (IBL) has received relatively little attention, and the present paper explores the applicability of these methods in the field of hydrological forecasting. Their performance is compared with that of ANNs, M5 model trees and conceptual hydrological models. Four short-term flow forecasting problems were solved for two catchments. Results showed that the IBL methods often produce better results than ANNs and M5 model trees, especially if used with the Gaussian kernel function. The study showed that IBL is an effective data-driven method that can be successfully used in hydrological forecasting.
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