Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet

Stergiou, 1997, Modelling and forecasting monthly fisheries catches: comparison of regression, univariate and multivariate time series methods, Fish. Res., 29, 55, 10.1016/S0165-7836(96)00482-1 Lloret, 2000, Time series modelling of landings in Northwest Mediterranean Sea, ICES J. Mar. Sci., 57, 171, 10.1006/jmsc.2000.0570 Pierce, 2003, Empirical modelling of interannual trends in abundance of squid (Loligo forbesi) in Scottish waters, Fish. Res., 59, 305, 10.1016/S0165-7836(02)00028-0 Koutroumanidis, 2006, Time series modelling of fishery landings using ARIMA models and fuzzy expected intervals software, Environ. Model. Softw., 21, 1711, 10.1016/j.envsoft.2005.09.001 Shabri, 2015, Fishery landing forecasting using wavelet-based autoregressive integrated moving average models, Math. Probl. Eng., 2015, 10.1155/2015/969450 Bako, 2013, Predictive modelling of pelagic fish catch in Malaysia using seasonal ARIMA models, Agr. For. Fish., 2, 136 Ghani, 2010, Stepwise multiple regression method to forecast fish landing, Procedia - Social. Behav. Sci., 8, 549, 10.1016/j.sbspro.2010.12.076 Cabreira, 2009, Artificial neural networks for fish-species identification, ICES J. Mar. Sci., 66, 1119, 10.1093/icesjms/fsp009 Hu, 2012, Fish species classification by color, texture and multi-class support vector machine using computer vision, Comput. Electron. Agr., 88, 133, 10.1016/j.compag.2012.07.008 Hugo, 2010, Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks, Fish. Res., 102, 115, 10.1016/j.fishres.2009.10.015 R. Larsen, H. lafsdottir, B. Ersbøll, Shape and texture based classification of fish species, in: Proceedings of the Scandinavian Conference on Image Analysis, 2009, pp. 745–749. J. Matai, R. Kastner, G.R. Cutter, D.A. Demer, Automated techniques for detection and recognition of fishes using Computer Vision algorithms, Report of the National Marine Fisheries Service Automated Image Processing Workshop (NOAA Technical Memorandum NMFS-F/SPO-121), retrieved from 〈https://swfsc.noaa.gov/publications/CR/2012/2012Matai.pdf〉, 2010. Mutasem, 2009, Fish recognition based on the combination between robust features selection, image segmentation and geometrical parameters techniques using Artificial Neural Network and Decision Tree, Int. J. Comput. Sci. Inform. Secur., 6, 215 K.A. Mutasem, B.O. Khairuddin, N. Shahrulazman, A. Ibrahim, Fish recognition based on features extraction from colour texture user back-propagation classifier, Journal of Theoretical and Applied Information Technology 2005–2010 JATIT, retrieved from 〈http://www.jatit.org/volumes/research-papers/Vol18No1/3Vol18No1.pdf〉. Ogunlana, 2015, Fish classification using support vector machine, Afr. J. Comput. ICT, 8, 75 Rova, 2012, One fish, two fish, butterfish, trumpeter: recognizing fish in underwater video, Mach. Vision. Appl., 404 Joo, 2011, Optimization of an artificial neural network for identifying fishing set positions from VMS data: an example from the Peruvian anchovy purse seine fishery, Ecol. Model., 222, 1048, 10.1016/j.ecolmodel.2010.08.039 Mendoza, 2010, Using classification trees to study the effects of fisheries management plans on the yield of Merluccius merluccius (Linnaeus, 1758) in the Alboran Sea (Western Mediterranean), Fish. Res., 102, 191, 10.1016/j.fishres.2009.11.012 Laë, 1999, Predicting fish yield of African lakes using neural networks, Ecol. Model., 120, 325, 10.1016/S0304-3800(99)00112-X Oliveira, 2015, The phycotoxins' impact on the revenue of the Portuguese artisanal dredge fleet, Mar. Policy, 52, 45, 10.1016/j.marpol.2014.10.022 Oliveira, 2014, Enhancing the performance of quota managed fisheries using seasonality information: the case of the Portuguese artisanal dredge fleet, Mar. Policy, 45, 114, 10.1016/j.marpol.2013.11.014 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 McCulloch, 1990, A logical calculus of the ideas immanent in nervous activity, B, Math. Biol., 52, 99, 10.1016/S0092-8240(05)80006-0 Rufino, 2010, Ecology of megabenthic bivalve communities from sandy beaches on the south coast of Portugal, Sci. Mar., 74, 163, 10.3989/scimar.2010.74n1163 Oliveira, 2016, Evaluating the influence of skipper skills in the performance of Portuguese artisanal dredge vessels, ICES J. Mar. Sci., 73, 2721, 10.1093/icesjms/fsw103 Gaspar, 2002, Depth segregation phenomenon in Donax trunculus (Bivalvia: donacidae) populations of the Algarve coast (southern Portugal), Sci. Mar., 66, 111, 10.3989/scimar.2002.66n2111