Flood Forecasting Using Hybrid SVM‑GOA Model: A Case Study

Sahoo, Abinash; Ghose, Dillip Kumar

doi:10.1007/978-981-19-0901-6_37

Cited by 2 publications

(1 citation statement)

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“…S. Guan et al have demonstrated that the EMD-PSO-ELM model can effectively measure landslide displacement [14]. In this study, both the genetic algorithm (GA) [15] and the locust optimization algorithm (GOA) [16] are utilized to automatically optimize VMD hyper-parameters. This serves to eliminate the influence of human factors and improve the efficiency of decomposition.…”

Section: Introductionmentioning

confidence: 99%

Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization

Ye,

Liu,

Xie

et al. 2024

Electronics

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The precise extraction of displacement time series for complex landslides poses significant challenges, and conventional landslide prediction models often overlook the deformation impacts of displacement triggers. To address this, we introduce a novel composite model tailored for predicting landslide displacement. This model employs Variational Mode Decomposition (VMD) to isolate each displacement component, with optimization achieved through the groupwise coupling algorithm. Subsequently, Grey correlation analysis (GRA) is applied to quantitatively assess the dynamic correlations between various triggering factors and landslide displacement. This analysis informs the construction of a feature set predicated on these correlation factors. Integrating the time-series VMD module into the standard Transformer architecture facilitates the prediction of landslide displacement. This integration allows for the extraction of critical time-evolution features associated with the displacement components. Ultimately, the predicted displacements are aggregated and reconstructed. We validate our model using the Bazimen landslide case study, analyzing displacement monitoring data from 1 January 2007, to 31 December 2012. The values of the root mean square error and the mean absolute percentage error were 1.86 and 4.85, respectively. This model offers a more nuanced understanding of the multifaceted causes and evolutionary dynamics underpinning landslide displacement and deformation, thereby markedly enhancing prediction accuracy.

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Section: Introductionmentioning

confidence: 99%