TY - CONF A1 - Karapanagiotis, Christos A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Temperature and humidity discrimination in Brillouin distributed fiber optic sensing using machine learning algorithms T2 - Optical Sensing and Detection VII N2 - In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection. T2 - SPIE Photonics Europe 2022 CY - Strasbourg, France DA - 03.04.2022 KW - Distributed Brillouin sensing KW - Machine learning KW - Artificial neural networks KW - Simultaneous temperature and humidity sensing KW - BOFDA PY - 2022 DO - https://doi.org/10.1117/12.2620985 VL - 12139 SP - 1 EP - 7 PB - SPIE AN - OPUS4-54861 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -