TY  - CONF
AV  - none
KW  - Clustering algorithms; Complex networks; Forecasting; Global optimization; Machine learning; Pipeline corrosion; Pipelines; Principal component analysis
KW  -  Comparatives studies; External corrosion; Machine-learning; Particle swarm; Pipeline corrosion; Pipeline infrastructure; Prediction modelling; Principal-component analysis; Support vector regressions; Swarm optimization
KW  -  Particle swarm optimization (PSO)
TI  - Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study
ID  - scholars18992
SP  - 57
N1  - 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
N2  - 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.
SN  - 9798350333312
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2023///
EP  - 60
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182729958&doi=10.1109%2fSENNANO57767.2023.10352550&partnerID=40&md5=afcf81920fad8240be62a0e3b3482a98
A1  - Amzar, H.
A1  - Haziq, M.I.
A1  - May, Z.
ER  -