Impact of noise on machine learning models for simultaneously predicting water and oil levels using optical fiber sensors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257822" target="_blank" >RIV/61989100:27240/25:10257822 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0030399225006474?ref=pdf_download&fr=RR-2&rr=9509b314ef0db36c" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0030399225006474?ref=pdf_download&fr=RR-2&rr=9509b314ef0db36c</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.optlastec.2025.113056" target="_blank" >10.1016/j.optlastec.2025.113056</a>
Alternative languages
Result language
angličtina
Original language name
Impact of noise on machine learning models for simultaneously predicting water and oil levels using optical fiber sensors
Original language description
We address the problem of simultaneously estimating the water-oil interface and total levels in three-phase separator tanks by applying three machine learning methods: Multilayer Perceptron (MLP), Kolmogorov-Arnold Networks (KAN), and Random Forest (RF). Data was collected from Fiber Bragg Grating-based optical sensors and processed to suppress outliers using the Local Outlier Factor algorithm. Hyperparameters for each model were optimized using Grid Search, and their performance was compared. The trained models were also exposed to scenarios with different levels of noise, and performance was evaluated. The results suggest that KAN performs effectively in predicting liquid levels, achieving a Root Mean Square Error of less than 3 mm and a Mean Absolute Percentage Error below 0.3% in scenarios without noise. Both MLP and KAN exhibit similar accuracy when the noise level is up to 1%. However, the MLP model outperforms KAN in higher noisy scenarios. On the other hand, the RF model shows the least effectiveness overall in noisy environments, though it does maintain a relatively stable maximum error across different noise levels. Therefore, we demonstrate that KAN has advantages in low-noise scenarios compared to conventional MLP and RF models. Conversely, MLP is more effective under higher noise conditions. These findings can aid in the research and development of monitoring systems.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Optics and Laser Technology
ISSN
0030-3992
e-ISSN
1879-2545
Volume of the periodical
2025
Issue of the periodical within the volume
189
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
nestránkováno
UT code for WoS article
001497325800001
EID of the result in the Scopus database
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