@inproceedings{scholars18992,
           title = {Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023},
           pages = {57--60},
            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},
             doi = {10.1109/SENNANO57767.2023.10352550},
            year = {2023},
        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. {\^A}{\copyright} 2023 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182729958&doi=10.1109\%2fSENNANO57767.2023.10352550&partnerID=40&md5=afcf81920fad8240be62a0e3b3482a98},
        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)},
            isbn = {9798350333312},
          author = {Amzar, H. and Haziq, M. I. and May, Z.}
}