Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i17/92308
Year: 2016, Volume: 9, Issue: 17, Pages: 1-11
Original Article
Hieu N. Duong1 , Hien T. Nguyen2* , Snasel Vaclav3 and Lee Sanghyuk4
1Faculty of Computer Science and Engineering, HCM University of Technology, Vietnam; [email protected] 2Faculty of Information Technology, Ton Duc Thang University, Vietnam; [email protected] 3Department of Computer Science, VSB-Technical University of Ostrava, Czech Republic; [email protected] 4 Xi’an Jiaotong-Liverpool University, Suzhou, China; [email protected]
*Author of Corresponding: Hien T. Nguyen Faculty of Information Technology, Ton Duc Thang University, Vietnam; [email protected]
Background/Objectives: Data-driven models such as Recurrent Fuzzy Neural Network (RFNN) have been proven to be great methods for modeling, characterizing and predicting various kinds of nonlinear hydrologic time series data such as rainfall, water quality and river runoff. In modeling and predicting river runoff, the most important advantage of datadriven models is that they do not need as much data as do physical models such as the Soil and Water Assessment Tool (SWAT). In Vietnam, most of data which are required by SWAT are not available, thus data-driven models seem to be more suitable for predicting river runoff than SWAT. The objective of this study is to investigate the performance of SWAT, RFNN and an improvement of RFNN (RFNN-GA), which is a hybrid of RFNN and Genetic Algorithm (GA) in predicting the runoff of Srepok River in Central Highland of Vietnam. Methods/Statistical Analysis: Coefficient of correlation (R2 ) and mean absolute relative error (MARE) are used to analysis and compare the performance of SWAT, RFNN and RFNN-GA. Findings: The experimental results demonstrate that RFNN and RFNN-GA give the performance better than that of SWAT and they are able to be applied to real applications. Among these methods, RFNN-GA is the most superior. Application/ Improvements: In the terms of MARE and R2 , RFNN-GA improves RFNN 0.9% and 2.2%, respectively; and improves SWAT 27.4% and 12.5%, respectively. RFNN-GA was deployed to predict the runoff of Srepok River in Central Highland of Vietnam.
Keywords: Srepok, Runoff, Prediction, Recurrent Fuzzy Neural Network, Genetic Algorithm
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