Fishing the waves: comparing GAMs and random forest to evaluate the effect of changing marine conditions on the energy performance of vessels
Veröffentlicht:
Nov 18, 2022
Abstract
The optimization of consumption and the reduction of gas emissions in fisheries rely on a thorough examination of all factors affecting the energy balance of fishing vessels. Engines, propellers, or the hydrodynamic characteristics of vessels and gears are unquestionably the primary factors affecting this balance, and an improvement in energy efficiency based on these factors is typically attained through technical modifications to existing technologies. Behavioral modifications, such as a reduction in operational speeds or the selection of closer fishing grounds, are another option. There may still be room for improvement in behavioral responses, for instance by adapting fishing strategies in response to changing weather and sea conditions. As far as the authors are aware, the influence of varying sea state and wind conditions on the energy expenditure of fishing vessels has not yet been investigated and is the focus of this research. In this study, wind and wave actions were associated with the observed activity of three fishing vessels operating in the northern Adriatic Sea: an OTB, a PTM, and a TBB trawler. The analysis made use of a comparison between two different approaches, generalized additive models (GAMs) and random forest, in order to quantify the significance of each variable on the response and generate consumption forecasts. In our analysis, the observed influence of predictors was significant albeit occasionally ambiguous. Wave height had the most obvious impact on energy expenditure, with the towing and gear handling phases being the most affected by wave action. Conversely, navigation seemed to be mostly unaffected by significant wave heights up to 1.5 meters, with unclear effects on consumption estimated above this threshold. The relationship between winds and fuel consumption was found to be nonlinear and ambiguous; hence, its significance should be investigated further.
Article Details
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COLOMBELLI, A., PULCINELLA, J., BONANOMI, S., NOTTI, E., MORO, F., & SALA, A. (2022). Fishing the waves: comparing GAMs and random forest to evaluate the effect of changing marine conditions on the energy performance of vessels. Mediterranean Marine Science, 23(4), 935–951. https://doi.org/10.12681/mms.29721
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Literaturhinweise
Akaike, H., 1998. Information theory and an extension of the maximum likelihood principle. p. 199-213. In: Parzen, E., Tanabe, K., Kitagawa, G. (Eds). Selected Papers of Hirotugu Akaike. Springer, New York.
Amante, C., Eakins, B.W., 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS, NGDC-24, 19 pp.
Bastardie, F., Nielsen, J.R., Andersen, B.S., Eigaard, O.R., 2010. Effects of fishing effort allocation scenarios on energy efficiency and profitability: an individual-based model applied to Danish fisheries. Fisheries Research, 106, 501-516.
Breiman, L., 2001. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Scences, 16, 199-231.
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A., 1984. Classification and regression trees. The Wadsworth statistics/ probability series. Chapman & Hall/CRC press, Boca Raton, Florida, 366 pp.
Buglioni, G., Notti, E., Sala, A., 2011. E-Audit: Energy use in Italian fishing vessels. p. 1043-1047. In: Sustainable Maritime Transportation and Exploitation of Sea Resources. Rizzuto & Guedes Soares (Eds).Taylor & Francis Group, London.
Corbett, J.J., Wang, H., Winebrake, J.J., 2009. The effectiveness and costs of speed reductions on emissions from international shipping. Transportation. Research. Part D: Transport and Environment. 14, 593-598.
Díaz-Uriarte, R., Alvarez de Andrés, S., 2006. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, 3. EU, 2013. Regulation No 1380/2013 of the European Parliament and of the Council of 11 December 2013 on the Common Fisheries Policy, amending Council Regulations (EC) No 1954/2003 and (EC) No 1224/2009 and repealing Council Regulations (EC) No 2371/2002 and (EC) No 639/2004 and Council Decision 2004/585/EC, n.d. 40.
Faraway, J.J., 2016. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC press, 413pp.
Fernández-López, J., Schliep, K., 2018. rWind: Download, edit and include wind data in ecological and evolutionary analysis. Ecography 42, 804-810.
Gabiña, G., Basurko, O.C., Notti, E., Sala, A., Aldekoa, S. et al., 2016. Energy efficiency in fishing: Are magnetic devices useful for use in fishing vessels? Applied Thermal Engineering, 94, 670-678.
González-Irusta, J.M., González-Porto, M., Sarralde, R., Arrese, B., Almón, B. et al. P., 2015. Comparing species distribution models: a case study of four deep sea urchin species. Hydrobiologia, 745, 43-57.
Hastie, T.J., Tibshirani, R.J., 1986. Generalized additive models (with discussion). Statistical Science, 1 (3), 297-318. Hastie, T.J., Tibshirani, R.J., 1990. Generalized additive models, vol. 43. Monographs on Statistics and Applied Probability. Chapman & Hall/CRC press, Boca Raton, Florida, 352 pp.
Henning, C., 2015. fpc: Flexible Procedures for Clustering. R package version 2.1-10.
Hijmans, R., van Etten, J., 2014. Raster: Geographic data analysis and modeling. R Package Version 517, 2-12.
Ho, T.K., 1995. Random decision forests. p. 278-282. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, 14-16 August 1995. Vol 1, IEEE.
James, G., Witten, D., Hastie, T., Tibshirani, R., 2013. An introduction to statistical learning. Springer, New York, 426 pp.
Liaw, A., Wiener, M., 2002. Classification and Regression by randomForest. R news, 2/3, 18-22.
Lin, Y., He, J., Li, K., 2018. Hull form design optimization of twin-skeg fishing vessel for minimum resistance based on surrogate model. Advances in Engineering Software, 123, 38-50.
Liu, X., Song, Y., Yi, W., Wang, X., Zhu, J., 2018. Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity. Journal of Construction Engineering and Management, 144, 6.
Mantari, J.L., Ribeiro e Silva, S., Guedes Soares, C., 2009. Variations on transverse stability of fishing vessels due to fishing gear pull and waves. In: Proceedings of the XXI Pan American Congress of Naval Engineering (COPINAVAL), Montevideo. COPINAVAL, Uruguay.
Marmion, M., Hjort, J., Thuiller, W., Luoto, M., 2008. A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland. Earth Surface Processes and Landforms, 33 (14), 2241-2254.
Neves, M.A.S., Pérez, N.A., Valerio, L., 1999. Stability of small fishing vessels in longitudinal waves. Ocean Engineering, 26, 1389-1419.
Neves, M.A.S., Perez, N., Lorca, O., Rodrıguez, C., 2003. Hull design considerations for improved stability of fishing vessels in waves. p. 291-304. Ιn: Proceedings of Eighth International Conference on the Stability of Ships and Ocean Vehicles (STAB’2003), Madrid, 15-19 September 2003. STAB’2003, Madrid.
Notti, E., Sala, A., 2012. On the opportunity of improving propulsion system efficiency for italian fishing vessels. In: Second International Symposium on Fishing Vessel Energy Efficiency, E-Fishing, Vigo, 22-24 May 2012. Spain.
Notti, E., Buglioni, G., Sala, A., 2012. Energy performance evaluation for fishing vessels. p. 85-94. In: Proceedings of the 17th International Conference on Ships and Shipping Research, Naples, 17-19 October 2012. NAV, Italy.
Notti, E., Figari, M., Sala, A., Martelli, M., 2019. Experimental assessment of the fouling control coating effect on the fuel consumption rate. Ocean Engineering, 188, 106233.
Oshiro, T.M., Perez, P.S., Baranauskas, J.A., 2012. How Many Trees in a Random Forest? p. 154-168. In: Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Computer Science.
Perner, P. (Ed.). Springer, Berlin, Heidelberg. Parente, J., Fonseca, P., Henriques, V., Campos, A., 2008. Strategies for improving fuel efficiency in the Portuguese trawl fishery. Fisheries Research, 93, 117-124.
Parker, R.W., Tyedmers, P.H., 2015. Fuel consumption of global fishing fleets: current understanding and knowledge gaps. Fish and Fisheries, 16, 684-696.
Parker, R.W.R., Blanchard, J.L., Gardner, C., Green, B.S., Hartmann, K., et al., 2018. Fuel use and greenhouse gas emissions of world fisheries. Nature Climate Change, 8, 333-337.
Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10 (1), 439- 446.
Pelletier, N., Audsley, E., Brodt, S., Garnett, T., Henriksson, P., et al., 2011. Energy intensity of agriculture and food systems. Annual Review of Environment and Resources, 36, 223-246.
Poos, J.J., Turenhout, M.N.J., van Oostenbrugge, H.A.E., Rijnsdorp, A.D., 2013. Adaptive response of beam trawl fishers to rising fuel cost. ICES Journal of Marine Science., 70, 675-684.
R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Ronen, D., 1982. The effect of oil price on the optimal speed of ships. Journal of the Operational Research Society, 33, 1035-1040.
Sala, A., Hansen, K., Lucchetti, A., Palumbo, V., 2008. Energy saving trawl in Mediterranean demersal fisheries. p. 961- 964. In: Ocean Engineering and Coastal Resources. Guedes Soares & Kolev (Eds). Taylor Francis Group, London.
Sala, A., De Carlo, F., Buglioni, G., Lucchetti, A., 2011. Energy performance evaluation of fishing vessels by fuel mass flow measuring system. Ocean Engineering, 38, 804-809.
Sala, A., Notti, E., Bonanomi, S., Pulcinella, J., Colombelli, A., 2019. Trawling in the Mediterranean: An Exploration of Empirical Relations Connecting Fishing Gears, Otterboards and Propulsive Characteristics of Fishing Vessels. Frontiers in Marine Science, 6, 534.
Sala, A., Damalas, D., Labanchi, L., Martinsohn, J., Moro, F. et al., 2022. Energy audit and carbon footprint in trawl fisheries. Scientific Data, 9, 428.
Sampson, D.B., 1991. Fishing tactics and fish abundance, and their influence on catch rates. ICES Journal of Marine Science, 48, 291-301.
Santiago, J.L., Ballesteros, M.A., Chapela, R., Silva, C., Nielsen, K.N. et al., 2015. Is Europe ready for a results-based approach to fisheries management? The voice of stakeholders. Marine Policy, 56, 86-97.
Schau, E.M., Ellingsen, H., Endal, A., Aanondsen, S.A., 2009. Energy consumption in the Norwegian fisheries. Journal of Cleaner Production, 17, 325-334.
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., Zeileis, A., 2008. Conditional variable importance for random forests. BMC Bioinformatics, 9, 307.
Swider, A., Pedersen, E., 2019. Data-Driven Methodology for the Analysis of Operational Profile and the Quantification of Electrical Power Variability on Marine Vessels. IEEE Transactions on Power Systems, 34, 1598-1609.
Swider, A., Langseth, H., Pedersen, E., 2019. Application of data- driven models in the analysis of marine power systems. Applied Ocean Research, 92, 101934.
Tello, M., Ribeiro e Silva, S., Guedes Soares, C., 2011. Seakeeping performance of fishing vessels in irregular waves. Ocean Engineering, 38, 763-773.
Thierry, N.N.B., Tang, H., Achile, N.P., Xu, L., Hu, F. et al., 2020a. Comparative study on the full-scale prediction performance of four trawl nets used in the coastal bottom trawl fishery by flume tank experimental investigation. Applied Ocean Research, 95, 102022.
Thierry, N.N.B., Tang, H., Liuxiong, X., You, X., Hu, F. et al., 2020b. Hydrodynamic performance of bottom trawls with different materials, mesh sizes, and twine thicknesses. Fisheries Research, 221, 105403.
Thrane, M., 2004. Energy Consumption in the Danish Fishery: Identification of Key Factors. Journal of Industrial Ecology, 8, 223-239.
Tsagarakis, K., Carbonell, A., Brčić, J., Bellido, J.M., Carbonara, P. et al., 2017. Old Info for a New Fisheries Policy: Discard Ratios and Lengths at Discarding in EU Mediterranean Bottom Trawl Fisheries. Frontiers in Marine Science, 4, 99.
Verhulst, N., Jochems, J., 1993. Final Confidential report for the project TE-1.102 hp NET’92. Research Project Financed by the Commission of the European Communities within the Frame of the EEC research Programme in the fisheries sector (“FAR”).
Wileman, D.A., 1984. Project” Oilfish”: Investigation of the Resistance of Trawl Gear. Danish Institute of Fisheries Technology. Technical Report n. NP-6750170, 123 pp.
Wileman, D.A., Hansen, K., 1988. Estimation of the drag of trawls of known geometry. Hirtshals Denmark: The Danish Fisheries Technology Institute, 51 pp.
Wood, S., 2018. Package ‘mgcv’. 2016. URL http://cran. r-project. org/web/packages/mgcv/mgcv. pdf. R package version (2018): 1-0.
Zacharioudaki, A., Ravdas, M., Korres, G., 2019. Mediterranean Production Centre MEDSEA_HINDCAST_ WAV_006_012, 51 pp. https://www.cmcc.it/wp-content/ uploads/2021/06/CMEMS-MED-QUID-006-012.pdf
