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

• Published in: Catena (Giessen) Vol. 178; pp. 172 - 188
• Main Authors: Shirzadi, Ataollah, Solaimani, Karim, Roshan, Mahmood Habibnejad, Kavian, Ataollah, Chapi, Kamran, Shahabi, Himan, Keesstra, Saskia, Ahmad, Baharin Bin, Bui, Dieu Tien
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2019
• Subjects: Alternating decision tree; GIS; Landslide susceptibility; Pixel and sample size; Uncertainty; GIS; Pixel and sample size; Uncertainty; Alternating decision tree; Landslide susceptibility
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2019.03.017

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. •Prediction accuracy of landslide modeling under various scenarios was assessed.•Best combination of sample size and raster resolution for preparing LSMs was introduced.•Best sample size for the raster resolution of 10 m was acquired as 60/40% and 70/30%.•Best sample size for the raster resolution of 20 m was obtained as 80/20% and 90/10%.•Raster resolution of 10 m with sample size of 70/30% obtained the highest accuracy.