Landslide risk assessment in slopes and hillsides. Methodology and application in a real case

Pozo, Alex Fernando Ruiz; Jaramillo, Haidee Yuladi; Amaya, Romel Jesús Gallardo

doi:10.15446/dyna.v86n208.72341

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…For such structures, it is necessary to achieve a stability coefficient of about SF = 1.5. The occurrence of a landslide if the SF > 1.5 condition is met is highly unlikely [33]. Both in the first and the second case, the safety factor value achieved is sufficient.…”

Section: Calculation Resultsmentioning

confidence: 95%

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Cement Stabilization of Waste from Contaminated Soils in Terms of Its Installation into Engineered Landfill

Kwiecień,

Podgórska,

Rybak

et al. 2023

Applied Sciences

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Remediation and preparation for development is a crucial aspect of the valorization of post-mining areas. This study is focused on technologies devoted to the cement stabilization of post-industrial waste and petroleum contaminated soils. Two case studies are presented. Case 1 is based on the stabilization works of waste from a closed chemical plant in order to turn it into an engineered landfill. The results form the basis for numerical studies of slope stability. The shorter case 2 is based on the laboratory testing of a soil–cement composite with regard to petroleum contamination and the application of active carbon to neutralize it in the course of deep soil mixing. Both cases, due to the use of slag cement, are not considered to be sustainable (due to a relatively high carbon footprint), but they represent current geotechnical practice and form a reference for a wide range of applications. Both cases show the positive impact of stabilization by means of the addition of a hydraulic binder. The effect of soil improvement is measurable in terms of the stability factor of landfill slopes. The effect of active charcoal addition seems to be a valuable improvement to standard deep soil mixing technology in the case of contaminated soil. The presented results, despite their local importance related to the cases under scrutiny, have an important educational and scientific value for the energy sector, where contaminated sites need to be valorized.

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Section: Calculation Resultsmentioning

confidence: 95%

“…For such structures, it is necessary to achieve a stability coefficient of about SF = 1.5. The occurrence of a landslide if the SF > 1.5 condition is met is highly unlikely [33].…”

Section: Calculation Resultsmentioning

confidence: 99%

Cement Stabilization of Waste from Contaminated Soils in Terms of Its Installation into Engineered Landfill

Kwiecień,

Podgórska,

Rybak

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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Enhancing Landslide Vulnerability Mapping Through Automated Fuzzy Logic Algorithm-Based Methodology

Bruzón,

Arrogante-Funes,

Álvarez-Ripado

et al. 2024

Geotech Geol Eng

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Advanced data mining techniques for landslide susceptibility mapping

Ibrahim

Mustaffa

Balogun

et al. 2021

Geomatics, Natural Hazards and Risk

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This paper describes the development and validation of landslides susceptibility models for mountainous regions using advanced data mining techniques. The investigation was carried out to ascertain the effectiveness of Naïve Bayes Multinomial (NBM) and Random Trees (RT) in landslide susceptibility mapping. The NBM is an advancement of the frequently used Naïve Bayes classifiers, while the RT was built to overcome the limitations of the traditional forest classifiers. A geospatial database for this investigation comprises 148 landslide locations influenced by ten ( 10) landslide conditioning factors. The factors (Slope Angle, Slopes Elevation, Slope Aspect, Plan curvature, Profile Curvature, Lithology, Soil type, Stream power index (SPI), Sediment transport index (STI), and Rainfall precipitation) were drawn using a Multi Collinearity Decision Making (MCDM) technique. A Frequency Ratio (FR) analysis was used to obtain the relative significance of the factors in the slides. Predictive models were also developed by quantifying these models using data mining techniques. A section of the entire geospatial data (70%) was used as training datasets, while the remaining part of the data (30%) was used to validate the trained datasets. SVM, RT, and NBM algorithms were used to produce predicted datasets from the training datasets. These predicted datasets were used to develop the Landslides Susceptibility Models. A comparative assessment between the two classifiers against the famous traditional learning algorithm, the Support vector machines (SVM), was conducted. Model performance evaluators such as the AUROC, RSME, F-measure, MAE, and ACC were employed to check the predictive capabilities and accuracies of the models. The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. Further analysis and comparison of the models reveal that the new data mining techniques are reliable for landslide susceptibility. Simultaneously, the traditional algorithm is also useful and remains relevant, especially with similar site conditions. This study has provided insights on better ARTICLE HISTORY

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Landslide risk assessment in slopes and hillsides. Methodology and application in a real case

Cited by 7 publications

References 9 publications

Cement Stabilization of Waste from Contaminated Soils in Terms of Its Installation into Engineered Landfill

Cement Stabilization of Waste from Contaminated Soils in Terms of Its Installation into Engineered Landfill

Enhancing Landslide Vulnerability Mapping Through Automated Fuzzy Logic Algorithm-Based Methodology

Advanced data mining techniques for landslide susceptibility mapping

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