Correction to: Integrating the Particle Swarm Optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model

Saha, Sunil; Saha, Anik; Roy, Bishnu; Sarkar, Raju; Bhardwaj, Dhruv; Kundu, Barnali

doi:10.1007/s12145-022-00892-7

Cited by 4 publications

(1 citation statement)

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“…Due to the nonlinear transformation of the inner product function, the optimal hyperplane satisfying the classification is searched in the high-dimensional feature space. PSO is a computational method that has been used for optimization processing, with the advantages of fast computing speed and easy implementation [50]. In this way, the PSO algorithm can be applied to the support vector machine to find the optimal SVM parameters through the particle swarm, and each particle moves iteratively to find the potentially optimal particle swarm, using the constant updating of the particles to find the overall optimal position and to determine the direction of its movement and speed.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Landslide Susceptibility Evaluation of Bayesian Optimized CNN Gengma Seismic Zone Considering InSAR Deformation

Deng,

Zuo,

et al. 2023

Applied Sciences

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Landslides are one of the most common geological disasters in China, characterized by suddenness and uncertainty. Traditional methods are not sufficient for the accurate identification, early warning, and forecasting of landslide disasters. As high-resolution remote sensing satellites and interferometric synthetic aperture radar (InSAR) surface deformation monitoring technology have been leaping forward, the traditional methods of landslide monitoring data sources are limited, and there have been few effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors, etc. In this study, an area extending 10 km from the VII isobar of the Gengma earthquake was taken as the study area, and 13 evaluation factors were screened out by integrating the factors of InSAR surface deformation, topography, and geological environment. Landslide susceptibility was evaluated through the Bayesian optimized convolutional neural network (BO-CNN), and the Bayesian optimized random forests (BO-RF) and particle swarm optimization support vector machines (PSO-SVM) models were selected for comparative analyses. The accuracy of the model was evaluated by using three indices, including the ROC curve, the AUC value, and the FR value. Specifically, the ROC curves of PSO-SVM, BO-RF, and BO-CNN were close to the upper-left corner, indicating excellent model performance. Moreover, the AUC values were computed as 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslides in the high susceptibility area of BO-CNN reached up to 14.9 and exceeded those of PSO-SVM and BO-RF, respectively. Furthermore, the mentioned values of the SVM and BO-RF models were 4.55 and 3.69 higher. The experimental results indicated that, compared with other models, the BO-CNN model used in this study had a better effect on landslide susceptibility evaluation, and the research results are of great significance to the disaster prevention and mitigation measures of local governments.

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Section: Support Vector Machinesmentioning

confidence: 99%