Although many studies have been carried out for estimating the afflux through modern straight deck bridge constrictions, little attention has been given to medieval arched bridge constrictions. Hydraulic Research Wallingford in the UK (Brown, P.M., 1988. Afflux at arch bridges. Report SR 182. Wallingford, UK: HR Wallingford) recently published a major coverage of both experimental and field afflux data obtained from arched bridge constrictions. The report pointed out that the present day formulas developed for estimating the bridge afflux are inadequate to apply to ancient arched structures. Therefore, this study aimed at developing new afflux methods for arched bridge constrictions using multi-layer perceptrons (MLP) neural networks, radial basis function-based neural networks (RBNN), generalised regression neural networks (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) model. Multiple linear and multiple nonlinear regression analyses were also used for comparison purposes. Mean square errors, mean absolute errors, mean absolute relative errors, average of individual ratios between predicted and actual values, and determination coefficients were used as comparison criteria for the evaluation of model performances. The test results showed that MLP, RBNN, GRNN, and ANFIS models gave reasonable accuracy when applied to both the field and experimental data collected by Hydraulic Research Wallingford.

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Abstract. Groundwater, which is a strategic resource in Turkey, is used for drinking-use, agricultural irrigation and industrial purposes. Population increase and total water consumption are constantly increasing. In order to meet the need for water, over-shoots from underground water have caused significant falls in groundwater level. Estimation of water level is important for planning an efficient and sustainable groundwater management. In this study, groundwater level, monthly mean precipitation and temperature observations of Turkish General Directorate of State Hydraulic Works (DSI) in Hatay, Amik Plain, Kumlu district were used between 2000 and 2015 years. The performance evaluation was done by creating Multi Linear Regression (MLR) and Artificial Neural Networks (ANN) models. The ANN model gave better results than the MLR model.

Two-dimensional (2-D) formulae for estimating discharge capacity of straight compound channels are reviewed and applied to overbank flows in straight fixed and mobile bed compound channels. The predictive capabilities of these formulae were evaluated using experimental data obtained from the small-scale University of Birmingham channel. Full details of these data and key references may be found at the following www.flowdata.bham.ac.uk (university website). 2-D formulae generally account for bed shear, lateral shear, and secondary flow effects via 3 coefficients f, λ and . In this paper, the secondary flow term ( ) used within the 2-D methods analysed here is ignored in all applications. Two different 2-D formulae almost give practically the same results for the same data when the secondary flow term is ignored. For overall test cases, the value of dimensionless eddy viscosity λ used in 2-D formulae was kept at 0·13 as recommended for open channels. 2-D formulae gave good predictions for most of the data sets studied in comparison with the traditional 1-D methods, namely the Single Channel Method (SCM) and the Divided Channel Method (DCM). The accuracy of predictions of 2-D formulae was increased by calibrating of λ value where the calibration was needed. For overall data, the average errors for each method were Lateral Division Methods (LDMs), with λ value of 0·13, 2·8%, DCM 14·3% and SCM −26·8 −26·8 −26·8%. The average error was 0·5% for LDMs with the calibrated values of λ.