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

Nsengiyumva, Jean Baptiste; Luo, Geping; Hakorimana, Egide; Mind’je, Richard; Gasirabo, Aboubakar; Mukanyandwi, Valentine

doi:10.1111/risa.13359

Cited by 14 publications

(6 citation statements)

References 66 publications

(155 reference statements)

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“…Moreover, compared with KC et al's study [41], we selected more shallow landslide sample data, with additional and more comprehensive influencing factors. In this study, the SINMAP model performed worse than the machine learning models, which was consistent with previous research results [23]. Compared with the RF, the SINMAP model underestimated the susceptibility of shallow landslides in the study area.…”

Section: Difference In Sensitivity Mapping Of Shallow Landslides Pred...supporting

confidence: 90%

“…The minimum AUCs of the SINMAP model training and validation datasets were 0.69 and 0.74, respectively. Thus, for predicting landslide susceptibility, the models were ranked as follows: the RF was best, the SVM and the Log were moderate, and the SINMAP model had the poorest performance but was still acceptable [23].…”

Section: Model Performance and Validationmentioning

confidence: 99%

“…A physical model is a mechanism model established according to the process of landslide occurrence [20,21]. Scholars at home and abroad have proposed many physical mechanism models, among which the SINMAP model is widely used [22,23]. The applicability of the SINMAP model in predicting the spatial distribution of shallow landslides induced by rainfall has been tested in several areas, and the results are reliable [21,24,25].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are few comparative studies between the machine learning model and the physical model in the evaluation of the vulnerability of shallow landslides. By comparing the models with different principles, it will help to highlight the advantages and limitations of the model in evaluating the mapping of shallow landslide susceptibility [23,51]. Therefore, in this study, machine learning methods and a physical model were selected to evaluate the stability of the largest loess tableland-Dongzhiyuan.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

et al. 2022

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Shallow landslides restrict local sustainable socioeconomic development and threaten human lives and property in loess tableland. Therefore, the appropriate creation of risk maps is critical for mitigating shallow landslide disasters. The first task to be done was to evaluate the vulnerability of shallow landslides based on a machine learning model (random forest (RF), a support vector machine (SVM) and logistic regression (Log)), and a physical model (SINMAP) in the loess tableland area. By comparing the differences, the best method for evaluating the vulnerability of shallow landslide was selected. The nonlinear response relationship between shallow landslides and environmental factors was quantified based on the frequency ratio. Multicollinearity analysis was used to identify 10 factors that were applied on ML to construct the spatial distribution model. The SINMAP model used a DEM and soil physical parameters to determine the stability coefficient of the study area. The results showed that (1) shallow landslides in Dongzhiyuan mainly occurred on shady slopes with an elevation of 1068–1249 m, a slope gradient of 36°–60° and a concave shape. The stream power and stream transport indexes increased with increasing rainfall erosion, making shallow landslides likely. The susceptibility of shallow landslides changed parabolically with the change in the NDVI and mainly occurred in grassland and shrubland. (2) The four methods performed similarly in predicting the sensitivity of shallow landslides. The high-incidence areas were on both sides of eroded gully slopes. The tableland and gully bottom areas were not prone to shallow landslides. (3) The highest area under the curve (AUC) values were generated from the RF training and validation datasets of 0.92 and 0.93, respectively, followed by SVM AUC values of 0.91 and 0.92, respectively; Log AUC values of 0.91 and 0.89, respectively, and the SINMAP model AUC values of 0.69 and 0.74, respectively. In conclusion, the RF model best predicted the susceptibility of shallow landslides in the study area. The results provide a scientific basis for disaster mitigation on the Loess Plateau.

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Section: Difference In Sensitivity Mapping Of Shallow Landslides Pred...supporting

confidence: 90%

Section: Model Performance and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

et al. 2022

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“…STI describes topographic variables of water and sediment transport in landslides 52 . Lithology is one of the basic factors affecting the occurrence of landslides 62,63 .…”

Section: Landslide Influencing Factorsmentioning

confidence: 99%

Evaluation of landslide susceptibility of the Ya’an–Linzhi section of the Sichuan–Tibet Railway based on deep learning

Chen

Zhuang

et al. 2022

Environ Earth Sci

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The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya'an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.

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Susceptibility Modeling and Mapping

Mind’je

2023

Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda

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

Cited by 14 publications

References 66 publications

Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

Evaluation of landslide susceptibility of the Ya’an–Linzhi section of the Sichuan–Tibet Railway based on deep learning

Susceptibility Modeling and Mapping

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