- PE&RC (2)
- Alterra - Soil, water and land use (1)
- Laboratory of Geo-information Science and Remote Sensing (1)
- Soil Geography and Landscape (1)
- Soil, Water and Land Dynamics (1)
- Mahmood Habibnejad Roshan (1)
- Ataollah Kavian (1)
- Saskia Keesstra (1)
- Mauro Rossi (1)
- Jalal Samia (1)
- Himan Shahabi (1)
- Ataollah Shirzadi (1)
- Karim Solaimani (1)
- Arnaud Temme (1)
- Dieu Tien Bui (1)
- Jakob Wallinga (1)
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 Bin ; Bui, Dieu Tien - \ 2019
Catena 178 (2019). - ISSN 0341-8162 - p. 172 - 188.
Alternating decision tree - GIS - Landslide susceptibility - Pixel and sample size - Uncertainty
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.
Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory
Samia, Jalal ; Temme, Arnaud ; Bregt, Arnold ; Wallinga, Jakob ; Guzzetti, Fausto ; Ardizzone, Francesca ; Rossi, Mauro - \ 2017
Landslides 14 (2017)2. - ISSN 1612-510X - p. 547 - 558.
Follow-up landslides - Landslide susceptibility - Path dependency - Roundness - Spatial association
Landslides are a major category of natural disasters, causing loss of lives, livelihoods and property. The critical roles played by triggering (such as extreme rainfall and earthquakes), and intrinsic factors (such as slope steepness, soil properties and lithology) have previously successfully been recognized and quantified using a variety of qualitative, quantitative and hybrid methods in a wide range of study sites. However, available data typically do not allow to investigate the effect that earlier landslides have on intrinsic factors and hence on follow-up landslides. Therefore, existing methods cannot account for the potentially complex susceptibility changes caused by landslide events. In this study, we used a substantially different alternative approach to shed light on the potential effect of earlier landslides using a multi-temporal dataset of landslide occurrence containing 17 time slices. Spatial overlap and the time interval between landslides play key roles in our work. We quantified the degree to which landslides preferentially occur in locations where landslides occurred previously, how long such an effect is noticeable, and how landslides are spatially associated over time. We also investigated whether overlap with previous landslides causes differences in landslide geometric properties. We found that overlap among landslides demonstrates a clear legacy effect (path dependency) that has influence on the landslide affected area. Landslides appear to cause greater susceptibility for follow-up landslides over a period of about 10 years. Follow-up landslides are on average larger and rounder than landslides that do not follow earlier slides. The effect of earlier landslides on follow-up landslides has implications for understanding of the landslides evolution and the assessment of landslide susceptibility.