TY  - JOUR
JF  - International Journal of River Basin Management
VL  - 16
Y1  - 2018///
A1  - Muhammad, M.M.
A1  - Wan Yusof, K.
A1  - Ul Mustafa, M.R.
A1  - Zakaria, N.A.
A1  - Ab Ghani, A.
N1  - cited By 5
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043341427&doi=10.1080%2f15715124.2018.1437740&partnerID=40&md5=c92e9d53d3e594362c7408a1af9d5628
AV  - none
SP  - 427
PB  - Taylor and Francis Ltd.
IS  - 4
TI  - Prediction models for flow resistance in flexible vegetated channels
N2  - The analysis of flow resistance due to vegetation remains an issue in the hydraulic industry, although it has been systematically studied for several decades, accurate prediction of the resistance is still a challenge. This is because most of the previous studies used synthetic vegetation to model flowâ?? vegetation interactions. This paper presents the applications of the artificial neural network (ANN) and gene expression programming (GEP) as advanced tools, to predict the flow resistance (n) of natural vegetation using a grassed swale and laboratory channel, irrespective of the grass height with relative to flow depth. To achieve this, hourly discharges and water depths were measured in the grassed swale for different rainfall events using the electromagnetic current metre. Experiments were performed in the laboratory channel using the same grass, in order to get additional data. From the results obtained regression equation was developed for predicting the flow resistance through the use of dimensional analysis. The regression equation obtained was compared with the established models of ANN and GEP. The results show that ANN and GEP models gave a better prediction of n-values, based on performance indices. However, the GEP model would be preferred as it produced a physical equation that can be used in engineering practice. © 2018 International Association for Hydro-Environment Engineering and Research.
SN  - 15715124
EP  - 437
ID  - scholars10457
ER  -