A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

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• 作者：Mohammad Ashrafi^a ; ^{mohammad021@e.ntu.edu.sg} ; ^{mashrafi.eng@gmail.com} ; Lloyd Hock Chye Chua^a ; ^b ; ^{lloyd.chua@deakin.edu.au} ; Chai Quek^c ; ^{ashcquek@ntu.edu.sg} ; Xiaosheng Qin^a ; ^d ; ^{xsqin@ntu.edu.sg}
• 关键词：Runoff forecasting ; Online ; Neuro-fuzzy model ; Self-Evolving Takagi-Sugeno-Kang ; Mekong River
• 刊名：Journal of Hydrology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：545
• 期：Complete
• 页码：424-435
• 全文大小：1658 K
• 卷排序：545

文摘

GSETSK is a fully-online model which does not requires a large training dataset. River flow forecasting in two catchments were conducted with GSETSK. GSETSK results compared favorably against physical models used as benchmark. GSETSK produces a more interpretable rule base compared to other neuro-fuzzy models. The study shows the applicability of GSETSK for forecasts with limited data.