INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT, vol.37, no.5, pp.882-905, 2021 (Peer-Reviewed Journal)
Article / Article
INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT
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Water resources management, Takagi-Sugeno fuzzy rule-based model, hydrological model, daily streamflow data, data gaps, ARTIFICIAL NEURAL-NETWORKS, TREE-RING RECONSTRUCTIONS, TAKAGI-SUGENO MODELS, TIME-SERIES, RIVER-BASIN, ERGENE RIVER, RAINFALL, REGRESSION, FLOW, IDENTIFICATION
Sustainable water resources management requires long time series of streamflow data. In this study, a Takagi-Sugeno fuzzy rule-based (FRB) model is developed to reconstruct long periods of missing daily streamflow data which is a common problem in developing countries. The FRB model uses observations of neighbouring stream gauges, and thus is advantageous regarding data and time requirement compared to physical models. With the proper set of inputs, the FRB model provides better estimates than the hydrological model at two of the studied four stream gauges in the Meric-Ergene Basin. Filling long data-gaps with FRB models will facilitate the development of realistic water management strategies.