%K Clustering algorithms; Complex networks; Forecasting; Global optimization; Machine learning; Pipeline corrosion; Pipelines; Principal component analysis, Comparatives studies; External corrosion; Machine-learning; Particle swarm; Pipeline corrosion; Pipeline infrastructure; Prediction modelling; Principal-component analysis; Support vector regressions; Swarm optimization, Particle swarm optimization (PSO)
%X This paper compares two models, PSO-ANN and PCASVR, for predicting pipeline corrosion. PSO-ANN uses Particle Swarm Optimization, PCA-SVR uses Principal Component Analysis and Support Vector Regression. Both models were tested on a dataset with corrosion-related features. PSO-ANN performed exceptionally well due to its global optimization and neural network's ability to handle complexity. PCA-SVR was competitive, especially with high-dimensional data, but slightly less accurate for complex issues. This study helps engineers and researchers choose models for better corrosion prediction and pipeline management. © 2023 IEEE.
%R 10.1109/SENNANO57767.2023.10352550
%D 2023
%J 2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023
%L scholars18992
%O cited By 0; Conference of 2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023 ; Conference Date: 26 September 2023 Through 27 September 2023; Conference Code:195657
%I Institute of Electrical and Electronics Engineers Inc.
%A H. Amzar
%A M.I. Haziq
%A Z. May
%T Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study
%P 57-60