Skip to Main content Skip to Navigation
New interface
Conference papers

Applying Recurrent Fuzzy Neural Network to Predict the Runoff of Srepok River

Abstract : Recurrent fuzzy neural network (RFNN) is proven to be a great method for modeling, characterizing and predicting many kinds of nonlinear hydrological time series data such as rainfall, water quality, and river runoff. In our study, we employed RFNN to find out the correlation between the climate data and the runoff of Srepok River in Vietnam and then to model and predict the runoff of Srepok River in the current, as well as in the future. In order to prove the advantage of RFNN, we compare RFNN with an environmental model called SWAT on the same dataset. We conduct experiments using the climate data and the daily river’s runoff data that have been collected in 22 years, ranging from 1900 to 2011. The experiment results show that the relative error of RFNN is about 0.35 and the relative error of SWAT is 0.44. It means that RFNN outperforms SWAT. Moreover, the most important advantage of RFNN when comparing with SWAT is that RFNN does not need much data as SWAT does.
Complete list of metadata

Cited literature [16 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Wednesday, November 30, 2016 - 10:51:24 AM
Last modification on : Saturday, June 1, 2019 - 11:36:05 AM
Long-term archiving on: : Monday, March 27, 2017 - 9:22:35 AM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Hieu Ngoc Duong, Quyen Thi Nguyen, Long Ta Bui, Hien T. Nguyen, Václav Snášel. Applying Recurrent Fuzzy Neural Network to Predict the Runoff of Srepok River. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. pp.55-66, ⟨10.1007/978-3-662-45237-0_7⟩. ⟨hal-01405559⟩



Record views


Files downloads