eprintid: 18992
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/89/92
datestamp: 2024-06-04 14:11:26
lastmod: 2024-06-04 14:11:26
status_changed: 2024-06-04 14:04:36
type: conference_item
metadata_visibility: show
creators_name: Amzar, H.
creators_name: Haziq, M.I.
creators_name: May, Z.
title: Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study
ispublished: pub
keywords: 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)
note: 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
abstract: 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.
date: 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182729958&doi=10.1109%2fSENNANO57767.2023.10352550&partnerID=40&md5=afcf81920fad8240be62a0e3b3482a98
id_number: 10.1109/SENNANO57767.2023.10352550
full_text_status: none
publication: 2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023
pagerange: 57-60
refereed: TRUE
isbn: 9798350333312
citation:   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.