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Akarsu akımlarının tahmini havza ve taşkın yönetiminde oldukça önemlidir. Doğrusallık göstermeyen ve birçok parametreye bağlı olan hidrolojik olayların tahminleri oldukça zor bir konudur. Bu çalışmada Mersin Lamas Nehri aylık ortalama akım verileri kullanılarak Yapay Sinir Ağları (YSA), Destek Vektör Makineleri (DVM) ve Derin Öğrenme (DÖ) optimizasyonları kullanılarak akım modellemesi yapılmış ve karşılaştırılmıştır. Sonuçlar incelendiğinde problemin çözümünde en iyi yöntemin DÖ olduğu YSA ve DVM yöntemlerinin ise buna göre daha başarısız sonuçlar verdiği gözlemlenmiştir. En iyi sonucu DÖ’ye ait 1 giriş modeli vermiştir (KOKH= 0.7647, OMH=0.7370, NSE=0.9724, OMBH=27.9326, R²=0.9962)., Estimation of stream flows is very important in basin and flood management. It is a very difficult topic to predict hydrological events that do not show linearity and depend on many parameters. In this study, streamflow modeling has been made and compared by using Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Deep Learning (DL) optimizations using monthly average streamflow data of Mersin Lamas River. When the results are examined, it has been observed that the best method in solving the problem is ANN and SVM methods, which give more unsuccessful results. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Nash Sutcliffe model Efficiency coefficient (NSE), Mean Absolute Relative Error (MARE) and Coefficient of Determination (R2) were used as comparison criteria. 1 input model of DL gave the best result (RMSE= 0.7647, MAE=0.7370, NSE=0.9724, MARE=27.9326, R²=0.9962).

The digital elevation model (DEM) is the name given to a digital structure used to indicate the surface. Determination of features such as elevation, basin slope and basin area are very important in engineering applications. These properties are determined by the DEM and their power to represent accuracy or truth is vital in engineering applications. In addition to the latitude (X), longitude(Y) coordinate information, altitude information is required, and intermediate values are determined by different methods for DEM. In this study, Mert River Basin Samsun (Turkey) was chosen as the application area. Heights are estimated from X, Y coordinate information. Three different Artificial Neural Networks, IDW and Kriging methods were used. Artificial Neural Networks (ANN) were analyzed with three different inputs. These are: (i) x coordinate information; (ii) y coordinate information; (iii) It is in the form of x and y coordinate information and are used Radial Based Artificial Neural Network, Multilayer Artificial Neural Network and Generalized Artificial Neural Network. X and Y coordinate information was used in IDW and Kriging interpolation methods. Results were evaluated using Coefficient of Determination (R²), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as comparison criteria. According to the modeling results: It was observed that the results of all methods reached a sufficient level of accuracy. Kriging method was found to be the most successful model, followed by IDW and ANN.