Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used...

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Bibliographic Details
Published in:Risk analysis 2019-11, Vol.39 (11), p.2576-2595
Main Authors: Nsengiyumva, Jean Baptiste, Luo, Geping, Hakorimana, Egide, Mind'je, Richard, Gasirabo, Aboubakar, Mukanyandwi, Valentine
Format: Article
Language:English
Subjects:
Comparative analysis
Conditioning
Digital Elevation Models
Disaster
Disaster management
Emergency preparedness
Environmental risk
landslide
Landslides
Landslides & mudslides
Mapping
Model accuracy
Modelling
Multiple criterion
natural hazard
Predictions
Risk
Risk assessment
risk map, Rwanda
Root-mean-square errors
Stability analysis
Validity
Values
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Summary:The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators—accuracy, precision, and the root mean square error (RMSE)—were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central‐east Africa.
ISSN:0272-4332
1539-6924
DOI:10.1111/risa.13359