Alternative solutions for long missing streamflow data for sustainable water resources management

Mesta B., AKGÜN Ö. B., KENTEL ERDOĞAN E.

INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT, vol.37, no.5, pp.882-905, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1080/07900627.2020.1799763
  • Journal Name: INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, IBZ Online, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, PAIS International, Pollution Abstracts, Public Affairs Index
  • Page Numbers: pp.882-905
  • Keywords: 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
  • Middle East Technical University Affiliated: Yes

Abstract

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.