Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon

Djukem, Wamba Danny Love; Braun, Anika; Wouatong, Armand Sylvain Ludovic; Guedjeo, Christian Suh; Dohmen, Katrin; Wotchoko, Pierre; Fernández-Steeger, Tomás Manuel; Havenith, Hans-Balder

doi:10.3390/ijerph17186795

Cited by 20 publications

(5 citation statements)

References 68 publications

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“…Actually, landslides are the result of the interaction of multiple geo-environmental factors and human activity, including geological lithology, structure (e.g., fractural zones, faults, and joints), elevation, slope, aspect, river, regolith, soil, land cover, rainfall, roads, and housing development. Djukem et al (2020) and Zhang Y. et al (2020) have discussed and successfully applied these factors for landslide hazard assessment. Hence, these geo-environmental factors will be necessary and taken into account for achieving our purpose in this study.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

Qin

et al. 2021

Front. Earth Sci.

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Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

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Section: Introductionmentioning

confidence: 99%

Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

Qin

et al. 2021

Front. Earth Sci.

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“…,(x n , y n ). Then, the calculation formula of the correlation coefficient among evaluation factors is [62,63]…”

Section: Correlations Analysis Of Landslide Conditioning Factorsmentioning

confidence: 99%

Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China

Qin

Yang

et al. 2021

Sustainability

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This study evaluated causative factors in landslide susceptibility assessments and compared the performance of five landslide susceptibility models based on the certainty factor (CF), logistic regression (LR), analytic hierarchy process (AHP), coupled CF–analytic hierarchy process (CF-AHP), and CF–logistic regression (CF-LR). Kaiyang County, China, has complex geological conditions and frequent landslide disasters. Based on field observations, nine influencing factors, namely, altitude, slope, topographic relief, aspect, engineering geological rock group, slope structure, distance to faults, distance to rivers, and normalized difference vegetation index, were extracted using the raster data model. The precision of the five models was tested using the distribution of disaster points for each grade and receiver operating characteristic curve. The results showed that the landslide frequency ratios accounted for more than 75% within the high and very high susceptibility zones according to the model prediction, and the AUC evaluating precision was 0.853, 0.712, 0.871, 0.873, and 0.895, respectively. The accuracy sequencing of the five models was CF-LR > CF-AHP > LR > CF > AHP, indicating that the CF-AHP and CF-LR models are better than the others. This study provides a reliable method for landslide susceptibility mapping at the county-level resolution.

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“…The effect of landslide mass density has also been investigated by Schuster & Highland (2003) and Djukem et al (2020). One of the main features of momentum transferring is the disintegration of the quiescent water surface into smaller droplets at the early stages due to the gravity.…”

Section: Effect Of Landslide Mass Densitymentioning

confidence: 99%

‪Impulse wave generation: a comparison of landslides of block and granular masses by coupled Lagrangian tracking using VOF over a set mesh

et al. 2021

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This paper presents the numerical results of impulsive waves generated by landslides of solid block, granular materials and heavy block sinking. An impulse product parameter P is developed and a wide range of effective parameters are studied. The volume-of-fluid (VOF) and overset mesh methods have been used to study landslide-generated tsunamis. Also, a Lagrangian tracking approach coupled with the VOF to simulate the granular movement. The effect of the water reservoir depth, the landslide height, the landslide density and the geometrical parameters on the wave height (elevation) has been investigated using the open-source OpenFOAM software. The results have been presented for dimensionless distances and the normalized geometry of the landslide in the range 5–7, 1–2, respectively. These numbers have been normalized the height of the landslide (a). According to the results of simulations, the tsunami formation process is divided into three stages, which were analyzed in detail by considering the interactions between the solid and the water reservoir. The Scott Russell wave has the highest impulse product parameter among the impulse wave mechanisms which is 58.6% of the total impulse production. In addition, the duration of the wave propagation has been computed based on the wave height.

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Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon

Cited by 20 publications

References 68 publications

Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China

‪Impulse wave generation: a comparison of landslides of block and granular masses by coupled Lagrangian tracking using VOF over a set mesh

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