Abstract
Elevation is a basic information of the earth, and different elevation models are provided to better understanding the earth and its different functions. However, it is not always possible to conduct a comprehensive survey in big areas and calculate all surface points. The best way is survey some points, then the elevation estimation is done using these points in each part of study area. The purpose of this paper is to use interpolation methods to estimate elevation. In such cases, different methods are used to interpolate and estimate points with an uncertain height. In this paper, the three usual methods are chosen and introduced then their performance are compared. These methods including: Inverse Distance Weighting (IDW), the Krige method or Kriging, and Artificial Neural Network (ANN). The results show that Artificial Intelligence with RMS = 5.9m is better in compare to Kriging with RMS = 7.2 and IDW with RMS = 9. The obtained result presents that in despite of its convenience, ANN provides DEMs with minimum errors.
Highlights
Elevation is necessary data in research involving studies of the earth's land surface [1]
Since digital elevation model (DEM) have a variety of applications, the quality of DEMs is essential [8]
70% of data is used to produce an elevation model, and the remaining points are used for validation and evaluating the results
Summary
Elevation is necessary data in research involving studies of the earth's land surface [1]. This data is a fundamental input parameter for determining slope [2], agricultural applications [3], modeling for telecommunications [4], process engineering problems [5, 6]. Digital elevation model (DEM) is one of the most used models that illustrate the earth surface. This elevation model is usually made up of equal-sized grid cells, which represent an elevation value. Since DEMs have a variety of applications, the quality of DEMs is essential [8]
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