Abstract:

In many engineering applications, it is needed topographical and surface topography is required to determine the appropriate accuracy. As applications based on digital elevation model case study of height values using horizontal and vertical position information may be determined by appropriate methods. In this study, Samsun Mert river basin and at the intersection of the city limits, the present progress has been providing horizontal and vertical elevation values of the point where the coordinates of the map information have been studied to predict. Multi-Layer Artificial Neural Networks (MLANN), and regression analysis were used. The study was tested in three different combinations. These include: (i) X coordinate of the estimated height information; (ii) the estimated height of the Y coordinate information; (iii) the estimated height information in the form of X and Y coordinates. As a comparison criterion Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the determination coefficients (R2) were used. When the models were compared, Artificial Neural Networks (ANN) model gave a better fit (fit-line) than Multiple Linear Regression (MLR) and linear regression (LR) models. The highest R² value (0.948) was found from the ANN model where the combination (iii) was used as input data. The lowest R² value (0.132) was obtained from the regression model where the combination (ii) was used as input data. As a result of modeling, heights have been estimated more successfully with the (iii) combination of ANN. Therefore, ANN was determined as an alternative method to regression analysis.

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