Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution

Ataollah Shirzadi, Karim Solaimani, Mahmood Habibnejad Roshan, Ataollah Kavian, Kamran Chapi, Himan Shahabi, Saskia Keesstra, Baharin Bin Ahmad, Dieu Tien Bui*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)

Abstract

Understanding landslide characteristics such as their locations, dimensions, and spatial distribution is of highly importance in landslide modeling and prediction. The main objective of this study was to assess the effect of different sample sizes and raster resolutions in landslide susceptibility modeling and prediction accuracy of shallow landslides. In this regard, the Bijar region of the Kurdistan province (Iran) was selected as a case study. Accordingly, a total of 20 landslide conditioning factors were considered with six different raster resolutions (10 m, 15 m, 20 m, 30 m, 50 m, and 100 m) and four different sample sizes (60/40%, 70/30%, 80/20%, and 90/10%) were investigated. The merit of each conditioning factors was assessed using the Information Gain Ratio (IGR) technique, whereas Alternating decision tree (ADTree), which has been rarely explored for landslide modeling, was used for building models. Performance of the models was assessed using the area under the ROC curve (AUROC), sensitivity, specificity, accuracy, kappa and RMSE criteria. The results show that with increasing the number of training pixels in the modeling process, the accuracy is increased. Findings also indicate that for the sample sizes of 60/40% (AUROC = 0.800) and 70/30% (AUROC = 0.899), the highest prediction accuracy is derived with the raster resolution of 10 m. With the raster resolution of 20 m, the highest prediction accuracy for the sample size of 80/20% (AUROC = 0.871) and 90/10% (AUROC = 0.864). These outcomes provide a guideline for future research enabling researchers to select an optimal data resolution for landslide hazard modeling.

Original languageEnglish
Pages (from-to)172-188
JournalCatena
Volume178
DOIs
Publication statusPublished - 1 Jul 2019

Keywords

  • Alternating decision tree
  • GIS
  • Landslide susceptibility
  • Pixel and sample size
  • Uncertainty

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    Shirzadi, A., Solaimani, K., Roshan, M. H., Kavian, A., Chapi, K., Shahabi, H., Keesstra, S., Ahmad, B. B., & Bui, D. T. (2019). Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution. Catena, 178, 172-188. https://doi.org/10.1016/j.catena.2019.03.017