Quantifying Topographic Ruggedness Using Principal Component Analysis

Habib, Maan

doi:10.1155/2021/3311912

Cited by 17 publications

(11 citation statements)

References 66 publications

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“…The assessment of a particular prediction model performance is a complex process (Habib, 2021a, b). In this study, the goodness-of-fit of the models to the dataset was assessed by computing the coefficient of determination, Eq.…”

Section: Methodsmentioning

confidence: 99%

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Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

Habib¹,

Bashir

Alsalman

et al. 2023

MMMS

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PurposeSlope stability analysis is essential for ensuring the safe design of road embankments. While various conventional methods, such as the finite element approach, are used to determine the safety factor of road embankments, there is ongoing interest in exploring the potential of machine learning techniques for this purpose.Design/methodology/approachWithin the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter.FindingsGenerally, the study results have shown that the proposed machine learning models provide rapid and accurate estimates of the safety factor of road embankments and are, therefore, promising alternatives to traditional methods.Originality/valueAlthough machine learning algorithms hold promise for rapidly and accurately estimating the safety factor of road embankments, few studies have systematically compared their performance with traditional methods. To address this gap, this study introduces a novel approach using advanced ensemble machine learning techniques for efficient and precise estimation of the road embankment safety factor. Besides, the study comprehensively assesses the performance of these ensemble techniques, in contrast with established methods such as the finite element approach and empirical models, demonstrating their potential as robust and reliable alternatives in the realm of slope stability assessment.

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Section: Methodsmentioning

confidence: 99%

“…, 2022) and the development of earth resource maps (Pourghasemi et al. , 2020; Habib, 2021a, b). Besides, several ANN models were created to evaluate slope stability (Sakellariou and Ferentinou, 2005; Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

Habib¹,

Bashir

Alsalman

et al. 2023

MMMS

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show abstract

“…These vectors are then decomposed into their x , y , and z components and used to calculate the VRM (c). Figure adapted from Sappington et al (2007) and Habib (2021).…”

Section: Methodsmentioning

confidence: 99%

MultiscaleDTM: An open‐source R package for multiscale geomorphometric analysis

Ilich

Misiuk

Lecours

et al. 2023

Transactions in GIS

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Digital terrain models (DTMs) are datasets containing altitude values above or below a reference level, such as a reference ellipsoid or a tidal datum over geographic space, often in the form of a regularly gridded raster. They can be used to calculate terrain attributes that describe the shape and characteristics of topographic surfaces. Calculating these terrain attributes often requires multiple software packages that can be expensive and specialized. We have created a free, open‐source R package, MultiscaleDTM, that allows for the calculation of members from each of the five major thematic groups of terrain attributes: slope, aspect, curvature, relative position, and roughness, from a regularly gridded DTM. Furthermore, these attributes can be calculated at multiple spatial scales of analysis, a key feature that is missing from many other packages. Here, we demonstrate the functionality of the package and provide a simulation exploring the relationship between slope and roughness. When roughness measures do not account for slope, these attributes exhibit a strong positive correlation. To minimize this correlation, we propose a new roughness measure called adjusted standard deviation. In most scenarios tested, this measure produced the lowest rank correlation with slope out of all the roughness measures tested. Lastly, the simulation shows that some existing roughness measures from the literature that are supposed to be independent of slope can actually exhibit a strong inverse relationship with the slope in some cases.

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“…Elevation difference is first summed up and then multiplied or squared. The multiplied or squared value is squared again to get the final result TRI [25] . The equation is developed according to Fig.…”

Section: Topographic Roughness Index (Tri)mentioning

confidence: 99%

Modelling hydrological factors from DEM using GIS

Chowdhury

2023

MethodsX

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Quantifying Topographic Ruggedness Using Principal Component Analysis

Cited by 17 publications

References 66 publications

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

MultiscaleDTM: An open‐source R package for multiscale geomorphometric analysis

Modelling hydrological factors from DEM using GIS

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