A comprehensive study was performed to compare flow rate, mean velocity, vertical velocity distribution, and locations where the maximum velocity, d m , occurs on standard Ogee-crested spillways using experimental and numerical models. Five different models were constructed from rigid foam according to the specifications of the United States Army Corps of Engineers (USACE). The velocity of the flow was recorded along the downstream curve of the model for all models with different non-dimensional head ratios H/H d of 0.50, 1.00, and 1.33. Particle Image Velocimetry (PIV) was used to measure the flow velocities. Velocity distributions were obtained by analyzing a series of captured images using Matlab codes. A commercially available Computational Fluid Dynamics (CFD) software package, Flow-3D, was used for modelling the experimental model setups. Flow-3D analyzes the Reynolds-averaged Navier-Stokes equations and is widely verified for use in the field of spillway flow analysis. The maximum difference between numerical and experimental results in mean velocity values that do not exceed 6.2% for all values of head ratios. The interpolated values of recorded maximum velocity by the PIV technique are smaller than those values numerically computed. In the lower d m locations, the percent difference between these regions reaches -8.65%; the upper locations are 2.87%. The vertical location ( d m ) drops to the lower location when the upstream head increases, and the distance from the spillway axis decreases linearly.

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An experimental study is made here to investigate the discharge coefficient for contracted rectangular Sharp crested weirs. Three Models are used, each with different weir width to flume width ratios (0.333, 0.5, and 0.666). The experimental work is conducted in a standard flume with high-precision head and flow measuring devices. Results are used to find a dimensionless equation for the discharge coefficient variation with geometrical, flow, and fluid properties. These are the ratio of the total head to the weir height, the ratio of the contracted weir width to the flume width, the ratio of the total head to the contracted width, and Reynolds and Weber numbers. Results show that the relationship between the discharge coefficient and these variables is a non-linear power function with a determination coefficient of 0.97. The importance and normalized importance analysis show that 56.3 % of the discharge coefficient variation is explained by the head-to-contracted width of the weir ratio followed by lower effects of the other variables, namely 16.5, 13.7, 12.4, and 1.2 % for contracted width to flume width ratio, Reynolds number, the head to the contracted width ratio, and Weber, respectively. The effect of the Weber number on the discharge coefficient is much lower than that of the Reynolds number.

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data set sub-division into training, testing and holdout data sub-sets, and different number of hidden nodes in the hidden layer. It is found that it is not necessary that the nearest station to the station under prediction has the highest effect; this may be attributed to the high differences in elevation between the stations. It can also found that the variance is not necessary has effect on the correlation coefficient obtained.

A coupled artificial neural network model with a genetic algorithm optimization model is developed for a practical case of a single cutoff. The proposed cutoff is of a soil-embedded vertical part with an inclined extension. The model successfully found the optimum dimensions of the vertical and inclined parts, the optimum angle of inclination, and the optimum length of protection downstream of the cutoff for a factor of safety of 3 against piping. Two thousand one hundred cases are modeled first using Geo-studio software to find the required length of downstream protection against piping for different lengths of the vertical, inclined lengths of the cutoff, its angle of inclination, soil layer depth, and degree of anisotropy. Then the created data set was used to develop an Artificial Neural Network (ANN) model for finding the length of protection required. The ANN model showed high performance with a determination coefficient of (0.922). The genetic algorithm model needs a minimum number of randomly generated populations of 100000 and three crossover iterations to produce a stable optimum solution. Running the model for different practical cases showed that the optimum angle variation was low and fluctuated around 30o. This low angle variation was due to its lower effect on the downstream soil protection length compared to the other decision variables. At the same time, the other dimensions varied with input variables, such as the depth of the soil layer, the seepage driving head, and the degree of isotropy. For a degree of anisotropy (ratio of vertical to horizontal hydraulic gradient) less than 0.5, the results showed no need for protection against piping; hence it is recommended to use minimum dimensions for such a case. The coupled model can easily obtain the optimum dimensions for any given input. Importance analysis showed that the optimum length of the downstream protection was highly affected by the vertical and inclined length of the cutoff, while it was less affected by the angle of inclination. Correlation analysis supported the importance analysis.