Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution
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 m...
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my.utm.885732020-12-15T02:20:37Z http://eprints.utm.my/id/eprint/88573/ Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution Shirzadi, Ataollah Solaimani, Karim Roshan, Mahmood Habibnejad Kavian, Ataollah Chapi, Kamran Shahabi, Himan Keesstra, Saskia Ahmad, Baharin Dieu, Tien Bui TH434-437 Quantity surveying 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 and four different sample sizes 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 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. Elsevier B.V. 2019-07 Article PeerReviewed Shirzadi, Ataollah and Solaimani, Karim and Roshan, Mahmood Habibnejad and Kavian, Ataollah and Chapi, Kamran and Shahabi, Himan and Keesstra, Saskia and Ahmad, Baharin and Dieu, Tien Bui (2019) Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution. Catena, 178 . pp. 172-188. ISSN 0341-8162 http://dx.doi.org/10.1016/j.catena.2019.03.017 DOI:10.1016/j.catena.2019.03.017 |
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TH434-437 Quantity surveying Shirzadi, Ataollah Solaimani, Karim Roshan, Mahmood Habibnejad Kavian, Ataollah Chapi, Kamran Shahabi, Himan Keesstra, Saskia Ahmad, Baharin Dieu, Tien Bui Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution |
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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 and four different sample sizes 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 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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Article |
author |
Shirzadi, Ataollah Solaimani, Karim Roshan, Mahmood Habibnejad Kavian, Ataollah Chapi, Kamran Shahabi, Himan Keesstra, Saskia Ahmad, Baharin Dieu, Tien Bui |
author_facet |
Shirzadi, Ataollah Solaimani, Karim Roshan, Mahmood Habibnejad Kavian, Ataollah Chapi, Kamran Shahabi, Himan Keesstra, Saskia Ahmad, Baharin Dieu, Tien Bui |
author_sort |
Shirzadi, Ataollah |
title |
Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution |
title_short |
Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution |
title_full |
Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution |
title_fullStr |
Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution |
title_full_unstemmed |
Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution |
title_sort |
uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution |
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Elsevier B.V. |
publishDate |
2019 |
url |
http://eprints.utm.my/id/eprint/88573/ http://dx.doi.org/10.1016/j.catena.2019.03.017 |
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