Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa

Nsengiyumva, Jean Baptiste; Luo, Geping; Amanambu, Amobichukwu Chukwudi; Mind’je, Richard; Habiyaremye, Gabriel; Karamage, Fidele; Ochege, Friday Uchenna; Mupenzi, Christophe

doi:10.1016/j.scitotenv.2018.12.248

Cited by 70 publications

(18 citation statements)

References 60 publications

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“…These factors can be natural or man‐made, and they normally inform about what might have led to slope instability in the past. Studies confirmed that landslides could be triggered by similar causes that led to instability in the past (Abella & Van Westen, ; Nsengiyumva et al., ).…”

Section: Methodsmentioning

confidence: 83%

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

et al. 2019

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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.

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Section: Methodsmentioning

confidence: 83%

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

et al. 2019

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“…Overall, 66.8% had a slide depth <10 m and 94.04% were classified as being small to medium-sized (<100 × 104 m 3 ). Since most of the landslides were small enough against the working scale, their location information could be represented as point features [48,49]. Subsequently, the landslide inventory map of the study area was generated by plotting the centroids of those landslides ( Figure 1).…”

Section: Spatial Databasementioning

confidence: 99%

Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

Zhang

Mei

et al. 2020

Applied Sciences

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Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

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“…At present, there exist different classes of landslides in the literature, ranging from simple to very complex (Cruden and Varnes 1996;Nsengiyumva et al 2019). These include deep-seated, falls, topples, rotational, flows, lateral spreads, complex, shallow and translational landslides among others (Pradhan et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment

Nsengiyumva

Valentino

2020

Geomatics, Natural Hazards and Risk

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Sustainable landslide mitigation requires appropriate approaches to predict susceptible zones. This study compared the performance of Logistic Model Tree (LMT), Random Forest (RF) and Naïve-Bayes Tree (NBT) in predicting landslide susceptibility for the upper Nyabarongo catchment (Rwanda). 196 past landslides were mapped using field investigations. Thus, the inventory map was split into training and testing datasets. Fifteen predisposing factors were analysed and information gain (IG) technique was used to analyse the correlation between factors and observed landslides. Therefore, the area under receiver operating characteristic (AUROC) with other statistical estimators including accuracy, precision, and root mean square error (RMSE) were employed to compare the models. The AUC values were 78.7%, 80.9% and 82.4% for RF, LMT and NBT models, respectively. Additionally, the NBT produced the highest accuracy and precision values (0.799 and 0.745, respectively). Regarding RMSE values, the NBT model achieved an optimized prediction than RF and LMT models (0.301; 0.428 and 0.364, respectively). The results of the current study may inform further studies and appropriate landslide risk reduction and mitigation measures. They can also be instrumental for policy and decision making in regards with natural risk management.

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Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa

Cited by 70 publications

References 60 publications

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

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

Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment

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