Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 549566
Title Uncertainties of prediction accuracy in shallow landslide modeling : Sample size and raster resolution
Author(s) Shirzadi, Ataollah; Solaimani, Karim; Roshan, Mahmood Habibnejad; Kavian, Ataollah; Chapi, Kamran; Shahabi, Himan; Keesstra, Saskia; Ahmad, Baharin Bin; Bui, Dieu Tien
Source Catena 178 (2019). - ISSN 0341-8162 - p. 172 - 188.
DOI https://doi.org/10.1016/j.catena.2019.03.017
Department(s) Soil, Water and Land Use
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Alternating decision tree - GIS - Landslide susceptibility - Pixel and sample size - Uncertainty
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.

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