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Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
Open AccessArticle

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

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Department of Earth Sciences, Faculty of Sciences, University of Dschang, Dschang P.O. Box 67, Cameroon
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Department of Engineering Geology, Institute of Applied Geosciences, Faculty VI Planning Building Environment, Technische Universität Berlin, 10587 Berlin, Germany
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HTTC Bambili, University of Bamenda, Bamenda P.O. Box 39, Cameroon
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Geology Department-B18, Georisk and Environment, Faculty of Sciences, Liege University, B-4000 Liege, Belgium
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(18), 6795; https://doi.org/10.3390/ijerph17186795
Received: 10 August 2020 / Revised: 8 September 2020 / Accepted: 14 September 2020 / Published: 17 September 2020
(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)
In this work, we explored a novel approach to integrate both geo-environmental and soil geomechanical parameters in a landslide susceptibility model. A total of 179 shallow to deep landslides were identified using Google Earth images and field observations. Moreover, soil geomechanical properties of 11 representative soil samples were analyzed. The relationship between soil properties was evaluated using the Pearson correlation coefficient and geotechnical diagrams. Membership values were assigned to each soil property class, using the fuzzy membership method. The information value method allowed computing the weight value of geo-environmental factor classes. From the soil geomechanical membership values and the geo-environmental factor weights, three landslide predisposition models were produced, two separate models and one combined model. The results of the soil testing allowed classifying the soils in the study area as highly plastic clays, with high water content, swelling, and shrinkage potential. Some geo-environmental factor classes revealed their landslide prediction ability by displaying high weight values. While the model with only soil properties tended to underrate unstable and stable areas, the model combining soil properties and geo-environmental factors allowed a more precise identification of stability conditions. The geo-environmental factors model and the model combining geo-environmental factors and soil properties displayed predictive powers of 80 and 93%, respectively. It can be concluded that the spatial analysis of soil geomechanical properties can play a major role in the detection of landslide prone areas, which is of great interest for site selection and planning with respect to sustainable development at Mount Oku. View Full-Text
Keywords: soil geomechanical properties; geo-environmental factors; pearson correlation coefficient; statistical index information value method; fuzzy membership; receiver operator characteristic (ROC) curve; landslide susceptibility; disaster prevention soil geomechanical properties; geo-environmental factors; pearson correlation coefficient; statistical index information value method; fuzzy membership; receiver operator characteristic (ROC) curve; landslide susceptibility; disaster prevention
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Djukem, W.D.L.; Braun, A.; Wouatong, A.S.L.; Guedjeo, C.; Dohmen, K.; Wotchoko, P.; Fernandez-Steeger, T.M.; Havenith, H.-B. Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon. Int. J. Environ. Res. Public Health 2020, 17, 6795.

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