Sanz-González, J.C.; Jurado-Mc Allister, A.; Navarro-Martínez, M.; Martínez Álvarez-Castellanos, R.; Felis-Enguix, I.; Yazid, Y.; El-Mansouri, Y.; De Miquel-Moral, F.; Errachdi, H.; Juan-Licián, A. Sensing Offshore Aquaculture Infrastructures for Data-Driven Dynamic Stress Analysis. Fishes2024, 9, 61.
Sanz-González, J.C.; Jurado-Mc Allister, A.; Navarro-Martínez, M.; Martínez Álvarez-Castellanos, R.; Felis-Enguix, I.; Yazid, Y.; El-Mansouri, Y.; De Miquel-Moral, F.; Errachdi, H.; Juan-Licián, A. Sensing Offshore Aquaculture Infrastructures for Data-Driven Dynamic Stress Analysis. Fishes 2024, 9, 61.
Sanz-González, J.C.; Jurado-Mc Allister, A.; Navarro-Martínez, M.; Martínez Álvarez-Castellanos, R.; Felis-Enguix, I.; Yazid, Y.; El-Mansouri, Y.; De Miquel-Moral, F.; Errachdi, H.; Juan-Licián, A. Sensing Offshore Aquaculture Infrastructures for Data-Driven Dynamic Stress Analysis. Fishes2024, 9, 61.
Sanz-González, J.C.; Jurado-Mc Allister, A.; Navarro-Martínez, M.; Martínez Álvarez-Castellanos, R.; Felis-Enguix, I.; Yazid, Y.; El-Mansouri, Y.; De Miquel-Moral, F.; Errachdi, H.; Juan-Licián, A. Sensing Offshore Aquaculture Infrastructures for Data-Driven Dynamic Stress Analysis. Fishes 2024, 9, 61.
Abstract
The presence of escaped fish in aquaculture facilities as a result of harsh meteorological conditions (more pressing in a face of climate change) requires a better understanding of these dynamical behaviour through vigilant monitoring and validated numerical models. In this context, data from strain, stress sensors as well as meteorological and current sensors installed at an aquaculture farm in the Region of Murcia (Spain) were collected, processed and analysed. Among them, first results about the relationship between load and currents sensors are presented. Due to the complexity of the time series, various analyses were conducted to examine their interrelation, encompassing regression analysis of raw data and data segmented into different time intervals. Through this analysis, it was observed that employing distinct time windows better elucidated the data variability. Furthermore, an optimal data window of 240 data was identified, demonstrating significantly improved explanatory power, with the coefficient of determination (R2) increasing by approximately 0.8 depending on the section. This pave the way for optimizing the monitoring features that must be carried out to relate cause-and-effect variables in the behaviour of these off-shore infrastructures.
Keywords
off-shore aquaculture, escapes, gales, load sensors, current meters, linear regression, window data method.
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
