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Seed orchards are an important seed source because they have the most important link between tree breeding and plantation forestry. The aim of this study is to evaluate the potential of Adaptive Neuro-Fuzzy Inference Systems of artificial neural networks to predict the amount of cone in clonal seed orchards of Anatolian black pine. It was found that the coefficient of determination (R 2 ), the mean absolute error (MAE) and the root mean square error (RMSE) of the artificial neural network model were 0.85, 14.83 and 18.85, respectively. The amount of cone in clonal seed orchards of Anatolian black pine was predicted with high efficiency through artificial neural networks. Considering the lack of forestry studies based on the artificial neural network, this study will enable further researches to provide a new perspective.

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This article presents a three-dimensional CFD model and OpenCV code by comparing the flow over the spillway with the experimental data for use in spillway studies. A 1/200-scale experimental model of a real dam spillway was created according to Froude similarity. In the experimental studies, velocity and water depth were measured in four different sections determined in the spillway model. A three-dimensional ANSYS Fluent model of the spillway was created and the simulations of the flows occurring during the flood were obtained. In the numerical model, the two-phase VOF model and k-epsilon turbulence model are used. As a result of the numerical analysis, velocity, depth, pressure, and cavitation index values were examined. The velocity and depth values obtained with models were compared and a good agreement was found between the results. In addition, in this study, a different technique based on image processing is developed to calculate water velocity and depth. A floating object was placed in the spillway channel during the experiment and the movement of the object on the water was recorded with cameras placed at different angles. By using the object tracking method, which is an image processing technique, the position of the floating object was determined in each video frame in the video recordings. Based on this position, the velocity of the floating object and its perpendicular distance to the bottom of the channel was determined. Thus, an OpenCV-Python code has been developed that determines the velocity and water depth of the floating object depending on its position. The floating object velocity values obtained by the algorithm were compared with the velocity values measured during the experimental model, and new velocity correction coefficients were obtained for the chute spillways.

Regulation structures such as submerged vane are needed to reduce and eliminate environmental damage due to increased flooding in rivers. In particular, scours on the outer bank due to increased flow velocities cause the river bed to change and deteriorate. In this study, the effect on flow velocities was investigated experimentally by using 3-array submerged vane structures in areas close to the outer bank. The experimental vane results were performed in the open channel setup. The Computational Fluid Dynamics (CFD) results obtained with the numerical model were also verified and compared with experimental results. It has been observed that the CFD model gives results close to the real experimental results. The standard-based k-ε model was used as the turbulence model. In the outer meander, the 3-array submerged vane with a 3-vane structure was found to affect the flow velocity by 16–27% in the region behind the vane. The flow velocities were investigated along with depth using the CFD and found that the mean velocity was reduced by 14–21% along the depth. It is also recommended that submerged vane structures can be applied as an effective method in reducing flow velocities and directing flows.