Discharge estimation in a compound channel with converging and diverging floodplains using ANN–PSO and MARS Divyanshu Shekhar a , Bhabani Shankar Das a, *, Kamalini Devi b , Jnana Ranjan Khuntia b and Tapas Karmaker c a Department of Civil Engineering, National Institute of Technology, Patna 800005, India b Department of Civil Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India c Department of Civil Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India *Corresponding author. E-mail: bsd.nitrkl@gmail.com BSD, 0000-0003-1140-0432; KD, 0000-0002-5916-3256; JRK, 0000-0003-3943-4220 ABSTRACT The discharge estimation in rivers is crucial in implementing flood management techniques and essential flood defence and drainage sys- tems. During the normal flood season, water flows solely in the main channel. During a flood, rivers comprise a main channel and floodplains, collectively called a compound channel. Computing the discharge is challenging in non-prismatic compound channels where the floodplains converge or diverge in a longitudinal direction. Various soft computing techniques have nowadays become popular in the field of water resource engineering to solve these complex problems. This paper uses a hybrid soft computing technique – artificial neural network and particle swarm optimization (ANN–PSO) and multivariate adaptive regression splines (MARS) to model the discharge in non-prismatic compound open channels. The analysis considers nine non-dimensional parameters – bed slope, relative flow depth, relative longitudinal distance, hydraulic radius ratio, angle of convergence or divergence, flow aspect ratio, relative friction factor, and area ratio – as influencing factors. A gamma test is carried out to determine the optimal combination of input variables. The developed MARS model has produced satisfactory results, with a mean absolute percentage error (MAPE) of less than 7% and an R 2 value of more than 0.90. Key words: ANN–PSO, gamma test, MARS, non-prismatic compound channel HIGHLIGHTS • Using traditional methods to estimate discharge in non-prismatic compound channels provides unsatisfactory results. • Discharge is estimated in non-prismatic compound channels using two soft computing techniques ANN–PSO and MARS. • Influencing parameters for the prediction of discharge are identified using the Gamma test. • Different model performances have been carried out for different ranges of width ratio and relative flow depth. NOMENCLATURE Q fp discharges carried by the floodplain. Q measured discharge Q mc discharges carried by the main channel R fp hydraulic radius of the floodplain R mc hydraulic radius of the main channel S 0 bed slope of the channel n Manning’s roughness coefficient H total flow depth over the main channel h bankfull depth of the main channel P wetted perimeter R hydraulic radius A area of the compound channel f r relative friction factor A r area ratio This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/). © 2023 The Authors Journal of Hydroinformatics Vol 00 No 0, 1 doi: 10.2166/hydro.2023.145 corrected Proof Downloaded from http://iwaponline.com/jh/article-pdf/doi/10.2166/hydro.2023.145/1317077/jh2023145.pdf by guest on 05 November 2023