Digital elevation modeling using artificial neural networks, deterministic and geostatistical interpolation methods

Abstract

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.