Estimation of Streamflow Using Takagi-Sugeno Fuzzy Rule-Based Model

8 pages•Published: September 20, 2018

Abstract

In this study, a tool is developed to estimate streamflow at Guvenc River, Ankara by using Takagi-Sugeno
(TS) Fuzzy Rule-Based (RB) model. The model takes precipitation and runoff at time t as predictor (input)
and estimates the runoff at time t + 1. The approach used to generate the TS RB model is based on density
based clustering. Each cluster center is used to generate a fuzzy rule that represents the system behaviour.
Satisfactory results are obtained especially after including the seasonal behaviour of streamflow time series
into the model.

Keyphrases: clustering, streamflow estimation, takagi sugeno fuzzy rule based model

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 18-25.

Links:	https://easychair.org/publications/paper/8g1c
	https://doi.org/10.29007/vzsj

BibTeX entry

@inproceedings{HIC2018:Estimation_Streamflow_Using_Takagi,
  author    = {Omer Burak Akgun and Elcin Kentel},
  title     = {Estimation of Streamflow Using Takagi-Sugeno Fuzzy Rule-Based Model},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/8g1c},
  doi       = {10.29007/vzsj},
  pages     = {18-25},
  year      = {2018}}

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