Stability Prediction Model of Transmission Tower Slope Based on ISCSO-SVM DOI Open Access
Zilong Zhang, Xiaoliang Liu, Yanhai Wang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 14(1), P. 126 - 126

Published: Dec. 31, 2024

Landslides induced by heavy rainfall are common in southern China and pose significant risks to the safe operation of transmission lines. To ensure reliability line operations, this paper presents a stability prediction model for tower slopes based on Improved Sand Cat Swarm Optimization (ISCSO) algorithm Support Vector Machine (SVM). The ISCSO is enhanced with dynamic reverse learning triangular wandering strategies, which then used optimize kernel penalty parameters SVM, resulting ISCSO-SVM model. In study, typical slope as case database generated through orthogonal experimental design Geo-studio simulations. addition traditional input features, an additional input—transmission catchment area—is incorporated, stable state set predicted output. results demonstrate that achieves highest accuracy, smallest errors across all metrics. Specifically, compared standard MAPE, MAE, RMSE values reduced 70.96%, 71.41%, 57.37%, respectively. effectively predicts slopes, thereby ensuring

Language: Английский

Advanced tree-based machine learning methods for predicting the seismic response of regular and irregular RC frames DOI
Ahmet Demir, Emrehan Kutluğ Şahin, Selçuk Demir

et al.

Structures, Journal Year: 2024, Volume and Issue: 64, P. 106524 - 106524

Published: May 11, 2024

Language: Английский

Citations

11
Development of a framework for the prediction of slope stability using machine learning paradigms DOI

K. C. Rajan,

Milan Aryal,

Keshab Sharma

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: July 25, 2024

Language: Английский

Citations

9
Addressing limitations of the K-means clustering algorithm: outliers, non-spherical data, and optimal cluster selection DOI Creative Commons
Iliyas Karim Khan, Hanita Daud,

Nooraini Binti Zainuddin

et al.

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(9), P. 25070 - 25097

Published: Jan. 1, 2024

<p>Clustering is essential in data analysis, with K-means clustering being widely used for its simplicity and efficiency. However, several challenges can affect performance, including the handling of outliers, transformation non-spherical into a spherical form, selection optimal number clusters. This paper addressed these by developing enhancing specific models. The primary objective was to improve robustness accuracy presence issues. To handle this research employed winsorization method, which uses threshold values minimize influence extreme points. For KROMD method introduced, combines Manhattan distance Gaussian kernel. approach ensured more accurate representation data, facilitating better performance. third focused on gap statistic selecting achieved standardizing expected value reference using an exponential distribution, providing reliable criterion determining appropriate Experimental results demonstrated that effectively handles leading improved stability. significantly enhanced converting achieving level 0.83 percent execution time 0.14 per second. Furthermore, outperformed other techniques clusters, 93.35 0.1433 These advancements collectively enhance performance clustering, making it robust effective complex analysis tasks.</p>

Language: Английский

Citations

6
The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction DOI Creative Commons
Selçuk Demir, Emrehan Kutluğ Şahin

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(8), P. 3273 - 3290

Published: June 13, 2024

Abstract In the field of data engineering in machine learning (ML), a crucial component is process scaling, normalization, and standardization. This involves transforming to make it more compatible with modeling techniques. particular, this transformation essential ensure suitability for subsequent analysis. Despite application many conventional relatively new approaches ML, there remains conspicuous lack research, particularly geotechnical discipline. study, ML-based prediction models (i.e., RF, SVR, Cubist, SGB) were developed estimate undrained shear strength (UDSS) cohesive soil from perspective wide range data-scaling methods. Therefore, work presents novel ML framework based on Cubist regression method predict UDSS soil. A dataset including six different features one target variable used building models. The performance was examined considering impact pre-processing issue. For that purpose, scaling methods, namely Range, Z-Score, Log Transformation, Box-Cox, Yeo-Johnson, generate results then systematically compared using sampling ratios understand how model varies as various scaling/transformation methods algorithms combined. It observed or had considerable limited effects depending algorithm type ratio. Compared SGB models, provided higher metrics after applying steps. Box-Cox transformed yielded best among other an R 2 0.87 90% training set. Also, generally when transformed-based Log, Yeo-Johnson) than scaled-based Range Z-Score) show has potential prediction, have impacts predictive capacity evaluated

Language: Английский

Citations

5
Stability prediction of multi-material complex slopes based on self-attention convolutional neural networks DOI
Mansheng Lin,

Xuedi Chen,

Gongfa Chen

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 14, 2024

Language: Английский

Citations

5
Developing machine learning-based ground motion models to predict peak ground velocity in Turkiye DOI
Fahrettin Kuran, Gülüm Tanırcan, Elham Pashaei

et al.

Journal of Seismology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 5, 2024

Language: Английский

Citations

3
An innovative machine learning approach for slope stability prediction by combining shap interpretability and stacking ensemble learning DOI
Selçuk Demir, Emrehan Kutluğ Şahin

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Language: Английский

Citations

0
Outlier Detection of Slope Deformation Monitoring Data based on WMA-3σ DOI Creative Commons
Bin Li, Xingping Bai, Huanhuan Gao

et al.

Atlantis highlights in engineering/Atlantis Highlights in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 189 - 199

Published: Jan. 1, 2024

The outliers in slope deformation monitoring data often contain important information.The influence of external environment, the failure structure and instrument are reasons for outliers.Rapid accurate detection is not only basic work analysis calculation, but also an measure to find out whether safe time.Slope time series.The short-term changes smooth stable with strong autocorrelation.In this paper, adaptive weight calculation method was proposed Weighted Moving Average (WMA) algorithm.The algorithm can estimate measured high precision without being affected by outliers.Then, difference sequence between estimated calculated, mirror processing sequence.In order eliminate asymmetric distribution caused trend data.Finally, sequences after were detected using 3σ criterion.Thus outlier realized.Through example analysis, WMA-3σ accurately detect data.It has reference significance real-time efficient intelligent analysis.

Language: Английский

Citations

0
Application of a semi-supervised technique for identifying unstable mine slopes DOI
Rudinei Martins de Oliveira, Tatiana Barreto dos Santos, Ladir Antônio da Silva Júnior

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Language: Английский

Citations

0
Stability Prediction Model of Transmission Tower Slope Based on ISCSO-SVM DOI Open Access
Zilong Zhang, Xiaoliang Liu, Yanhai Wang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 14(1), P. 126 - 126

Published: Dec. 31, 2024

Landslides induced by heavy rainfall are common in southern China and pose significant risks to the safe operation of transmission lines. To ensure reliability line operations, this paper presents a stability prediction model for tower slopes based on Improved Sand Cat Swarm Optimization (ISCSO) algorithm Support Vector Machine (SVM). The ISCSO is enhanced with dynamic reverse learning triangular wandering strategies, which then used optimize kernel penalty parameters SVM, resulting ISCSO-SVM model. In study, typical slope as case database generated through orthogonal experimental design Geo-studio simulations. addition traditional input features, an additional input—transmission catchment area—is incorporated, stable state set predicted output. results demonstrate that achieves highest accuracy, smallest errors across all metrics. Specifically, compared standard MAPE, MAE, RMSE values reduced 70.96%, 71.41%, 57.37%, respectively. effectively predicts slopes, thereby ensuring

Language: Английский

Citations

0