Introducing a Novel Digital Elevation Model Using Artificial Neural Network Algorithm


  • Saeed Behzadi Department of Civil Engineering Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran
  • Amin Jalilzadeh MSc Student in Geographic Information Systems, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran



Digital Train Model, Artificial Neural Network, Kriging


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.




How to Cite

Behzadi, S., & Jalilzadeh, A. (2020). Introducing a Novel Digital Elevation Model Using Artificial Neural Network Algorithm. Civil Engineering Dimension, 22(2), 47-51.