Landslide susceptibility mapping: a practitioner’s view

Hearn, G. J.; Hart, A. B.

doi:10.1007/s10064-019-01506-1

Cited by 43 publications

(16 citation statements)

References 37 publications

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“…is differential equation needs to be solved by integral, and only a few cases have analytical solutions, such as ignoring the soil's own weight, and more general cases require numerical methods. e vertical strip method and the oblique strip-mode energy method have their own characteristics, and simple discrete methods are used to solve the corresponding safety factors [20]. e vertical striping method was developed earlier, and the concept is simpler and more straightforward, but it is not as strict as the diagonal striping method from the theoretical basis.…”

Section: Results Analysismentioning

confidence: 99%

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

2021

Advances in Civil Engineering

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Mine rock slope stability rating is the process of assessing its stability probability, analyzing its failure mode, and then determining its stability rating by location matching principle and scale matching principle, having important theoretical and practical significance in putting forward reasonable restoration suggestions. In recent years, rapidly developing artificial intelligence methods not only have the characteristics of highly nonlinear mapping, self-organization, and self-adaptation but also mostly apply probability optimal strategy and, hence, bypass the problem of solving function derivatives, having higher efficiency and feasibility in solving the problems of global optimal solution and local optimal solution. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of mine rock slope stability rating, elaborated the development background, current status, and future challenges of artificial intelligence methods, introduced the principles of the limit equilibrium method and finite element method, constructed an artificial intelligence-based mine rock slope stability calculation model, performed slope stability influencing factors’ analysis and optimization model parameter selection, proposed a mine rock slope stability rating algorithm based on artificial intelligence, analyzed the rating algorithm design and rating algorithm implementation, and finally conducted a case analysis and its discussion. The study results show that the artificial intelligence-based mine rock slope stability rating method can make full use of computer advantages to have larger capacity and faster calculation speed and solve to the greatest extent the randomness, timeliness, ambiguity, and other uncertain characteristics of mine rock slope stability by a large number of random sampling for finding the optimal value of the objective function. The study results of this paper provide a reference for further research studies on the stability rating of mine rock slopes based on artificial intelligence.

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Section: Results Analysismentioning

confidence: 99%

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

2021

Advances in Civil Engineering

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“…Many landslide parameters and increasingly sophisticated statistical methods have been used in landslide research over the past decades [11]. However, poor representation of actual ground conditions hampers research results to be applied in practice [12]. Daniel et al conducted a landslide susceptibility assessment with the bivariate statistical method, which reduces the number of input parameters from 13 to 6.…”

Section: Application In Disaster Risk Reductionmentioning

confidence: 99%

Editorial: Big Earth Data for Disaster Risk Reduction

et al. 2021

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“…One of the few widely accepted principles in landslide prediction is that the conditioning factors that caused past and recent landslides will likely be the ones triggering future landslides [4]. Also, according to previous studies [54][55][56], a good selection of landslide conditioning factors is one of the vital requirements to have accurate landslide susceptibility maps. Based on an analysis performed by [32], other previous works [24,52], and the availability of data in the study region, 14 conditioning factors were chosen for this study.…”

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Dang

Hoang

Nguyen

et al. 2020

Forests

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This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

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Landslide susceptibility mapping: a practitioner’s view

Cited by 43 publications

References 37 publications

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

Stability Rating of Mine Rock Slope Based on Artificial Intelligence

Editorial: Big Earth Data for Disaster Risk Reduction

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

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