Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1085–1090, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1085-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1085–1090, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1085-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

LANDSLIDE HAZARD MAPPING USING A RADIAL BASIS FUNCTION NEURAL NETWORK MODEL: A CASE STUDY IN SEMIROM, ISFAHAN, IRAN

H. Yavari1, P. Pahlavani1, and B. Bigdeli2 H. Yavari et al.
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • 2School of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

Keywords: Landslide, RBF neural network, Logistic Regression

Abstract. In this paper, Radial Basis Function (RBF) Neural Network and Logistic Regression (LR) models were proposed for hazard prediction of landslides in a part of the Semirom area (Iran) to compare their accuracy and performance. For this purpose, a spatial database of the study area was prepared that consists of 68 landslide locations and 11 influencing information layers including slope, aspect, profile curvature, plan curvature, distance from faults, distance from roads, distance from residential regions, distance from rivers, land use, lithology and rainfall. Landslide hazard maps were prepared for the study area by applying the proposed algorithms. Performance of the models was assessed using the Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC). The coefficient of determination (R2), the root mean square error (RMSE), and the Normal Root Mean Square Error (NRMSE) were calculated for proposed methods. The outcomes showed that the RBF Neural Network has the highest R2 (0.8224), in comparison to that of the LR model (0.5365). Also, the ROC plots, RMSEs and NRMSEs showed that the proposed RBF Neural Network is much better than the LR model. Consequently, it can be concluded that the RBF Neural Network is the best regression model in this study and it can be considered as a capable method for landslide hazard mapping in landslide-susceptible areas.