relation: https://khub.utp.edu.my/scholars/18992/
title: Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study
creator: Amzar, H.
creator: Haziq, M.I.
creator: May, Z.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Amzar, H. and Haziq, M.I. and May, Z.  (2023) Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182729958&doi=10.1109%2fSENNANO57767.2023.10352550&partnerID=40&md5=afcf81920fad8240be62a0e3b3482a98
relation: 10.1109/SENNANO57767.2023.10352550
identifier: 10.1109/SENNANO57767.2023.10352550