Application of Machine Learning Techniques in Aquaculture

ABSTRACT: In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications

Current Status of Forecasting Toxic Harmful Algae for the North-East Atlantic Shellfish Aquaculture Industry

Across the European Atlantic Arc (Scotland, Ireland, England, France, Spain, and Portugal) the shellfish aquaculture industry is dominated by the production of mussels, followed by oysters and clams. A range of spatially and temporally variable harmful algal bloom species (HABs) impact the industry through their production of biotoxins that accumulate and concentrate in shellfish flesh, which negatively impact the health of consumers through consumption. Regulatory monitoring of harmful cells in the water column and toxin concentrations within shellfish flesh are currently the main means of warning of elevated toxin events in bivalves, with harvesting being suspended when toxicity is elevated above EU regulatory limits. However, while such an approach is generally successful in safeguarding human health, it does not provide the early warning that is needed to support business planning and harvesting by the aquaculture industry. To address this issue, a proliferation of web portals have been developed to make monitoring data widely accessible. These systems are now transitioning from “nowcasts” to operational Early Warning Systems (EWS) to better mitigate against HAB-generated harmful effects. To achieve this, EWS are incorporating a range of environmental data parameters and developing varied forecasting approaches. For example, EWS are increasingly utilizing satellite data and the results of oceanographic modeling to identify and predict the behavior of HABs. Modeling demonstrates that some HABs can be advected significant distances before impacting aquaculture sites. Traffic light indices are being developed to provide users with an easily interpreted assessment of HAB and biotoxin risk, and expert interpretation of these multiple data streams is being used to assess risk into the future. Proof-of-concept EWS are being developed to combine model information with in situ data, in some cases using machine learning-based approaches. This article: (1) reviews HAB and biotoxin issues relevant to shellfish aquaculture in the European Atlantic Arc (Scotland, Ireland, England, France, Spain, and Portugal; (2) evaluates the current status of HAB events and EWS in the region; and (3) evaluates the potential of further improving these EWS though multi-disciplinary approaches combining heterogeneous sources of information.Versión del edito

Predictive based hybrid ranker to yield significant features in writer identification

The contribution of writer identification (WI) towards personal identification in biometrics traits is known because it is easily accessible, cheaper, more reliable and acceptable as compared to other methods such as personal identification based DNA, iris and fingerprint. However, the production of high dimensional datasets has resulted into too many irrelevant or redundant features. These unnecessary features increase the size of the search space and decrease the identification performance. The main problem is to identify the most significant features and select the best subset of features that can precisely predict the authors. Therefore, this study proposed the hybridization of GRA Features Ranking and Feature Subset Selection (GRAFeSS) to develop the best subsets of highest ranking features and developed discretization model with the hybrid method (Dis-GRAFeSS) to improve classification accuracy. Experimental results showed that the methods improved the performance accuracy in identifying the authorship of features based ranking invariant discretization by substantially reducing redundant features

Predictive Based Hybrid Ranker To Yield Significant Features In Writer Identification

The contribution of writer identification (WI) towards personal identification in biometrics traits is known because it is easily accessible, cheaper, more reliable and acceptable as compared to other methods such as personal identification based DNA, iris and fingerprint. However, the production of high dimensional datasets has resulted into too many irrelevant or redundant features. These unnecessary features increase the size of the search space and decrease the identification performance. The main problem is to identify the most significant features and select the best subset of features that can precisely predict the authors. Therefore, this study proposed the hybridization of GRA Features Ranking and Feature Subset Selection (GRAFeSS) to develop the best subsets of highest ranking features and developed discretization model with the hybrid method (Dis-GRAFeSS) to improve classification accuracy. Experimental results showed that the methods improved the performance accuracy in identifying the authorship of features based ranking invariant discretization by substantially reducing redundant features

Quantifying Spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach

Harmful algal blooms (HABs) intoxicate and asphyxiate marine life, causing devastating environmental and socio-economic impacts, costing at least $8bn/yr globally. Accumulation of phycotoxins from HAB phytoplankton in filter-feeding shellfish can poison human consumers, prompting harvesting closures at shellfish production sites. To quantify long-term intoxication risk from Dinophysis HAB species, we used historical HAB monitoring data (2009–2020) to develop a new modelling approach to predict Dinophysis toxin concentrations in a range of bivalve shellfish species at shellfish sites in Western Scotland, South-West England and Northern France. A spatiotemporal statistical modelling framework was developed within the Generalized Additive Model (GAM) framework to quantify long-term HAB risks for different bivalve shellfish species across each region, capturing seasonal variations, and spatiotemporal interactions. In all regions spatial functions were most important for predicting seasonal HAB risk, offering the potential to inform optimal siting of new shellfish operations and safe harvesting periods for businesses. A 10-fold cross-validation experiment was carried out for each region, to test the models’ ability to predict toxin risk at harvesting locations for which data were withheld from the model. Performance was assessed by comparing ranked predicted and observed mean toxin levels at each site within each region: the correlation of ranks was 0.78 for Northern France, 0.64 for Western Scotland, and 0.34 for South-West England, indicating our approach has promise for predicting unknown HAB risk, depending on the region and suitability of training data

Review of current evidence to inform selection of environmental predictors for active management systems in classified shellfish harvesting areas

Summary: This scoping report explores the potential influences on E.coli concentrations in Shellfish around the UK in the context of new management approache

Study of phytoplankton as food resource and toxicity risk for human health in offshore bivalve aquaculture in the Basque Country

237 p.El fitoplancton, como base de las redes tróficas marinas, es la principal fuente de energía para los bivalvos filtradores, entre otros. En el País Vasco, el reciente interés en el cultivo de bivalvos en aguas costeras llevó a la instalación de una planta piloto frente a la costa de Mendexa (Bizkaia). En este contexto, surge la necesidad de estudiar y conocer la dinámica y composición de la comunidad fitoplanctónica en la costa vasca, dada su implicación en el crecimiento de bivalvos. Para ello, se han utilizado dos fuentes de información. Por una parte, se ha analizado una serie temporal de más de diez años a lo largo de toda la costa vasca; y, por otra parte, se ha llevado a cabo un estudio específico en la planta piloto. En general, en términos de disponibilidad alimentaria para los mejillones, se han encontrado atributos fitoplanctónicos favorables para el crecimiento de mejillones, como son la presencia de blooms, la contribución de las diferentes fracciones de tamaño de célula y la dominancia de las diatomeas y los dinoflagelados, principalmente. En cuanto al riesgo de toxicidad en humanos asociado a especies tóxicas de fitoplancton mediante la ingesta de mejillones, la presencia de toxina en mejillón se detectó en el 60% de los casos, aunque sólo un 15% hubiera implicado un riesgo para la salud humana. Todos estos casos se dieron en primavera y se asociaron al ácido ocadaico, siendo Dinophysis acuminata la especie causante.Azti Tecnali

Direct effects of climate change on productivity of European aquaculture

Aquaculture managers and industry must take into account the impact of climate change on production and environmental quality to ensure that sector growth is sustainable over the coming decades, a key requirement for food security. The potential effects of climate change on aquaculture range from changes to production capacity in existing cultivation areas to changes in the areas themselves, which may become unsuitable for particular species, but also suitable for new species. The prediction of where and how such changes may occur is challenging, not least because the cultivated species may themselves exhibit plasticity, which makes it difficult to forecast the degree to which different locations and culture types may be affected. This work presents a modelling approach used to predict the potential effects of climate change on aquaculture, considering six key finfish and shellfish species of economic importance in Europe: Atlantic salmon (Salmo salar), gilthead seabream (Sparus aurata), sea bass (Dicentrarchus labrax), Pacific oyster (Crassostrea gigas), blue mussel (Mytilus edulis) and Mediterranean mussel (Mytilus galloprovincialis). The focus is on effects on physiology, growth performance and environmental footprint, and the resultant economic impact at the farm scale. Climate projections for present-day conditions; mid-century (2040–2060) and end-of-century (2080–2100) were extracted from regionally downscaled global climate models and used to force bioenergetic models. For each of those time periods, two different carbon concentration scenarios were considered: a moderate situation (IPCC RCP 4.5) and an extreme situation (IPCC RCP 8.5). Projected temperature changes will have variable effects on growth depending on the species and geographic region. From the case studies analysed, gilthead bream farmed in sea cages in the western Mediterranean was the most vulnerable, whereas offshore-suspended mussel culture in SW Portugal was least affected. Most of the marine finfish simulated were projected to have decreased feeding efficiency in both mid-century and end-of-century climate scenarios. Bivalve shellfish showed a decreasing trend with respect to most productivity parameters as climate change progresses, under both emission scenarios. As a general trend across species and regions, economic uncertainty is expected to increase under all future projections

HABreports: Online Early Warning of Harmful Algal and Biotoxin Risk for the Scottish Shellfish and Finfish Aquaculture Industries

We present an on-line early warning system that is operational in Scottish coastal waters to minimize the risk to humans and aquaculture businesses in terms of the human health and economic impacts of harmful algal blooms (HABs) and their associated biotoxins. The system includes both map and time-series based visualization tools. A “traffic light” index approach is used to highlight locations at elevated HAB/biotoxin risk. High resolution mathematical modelling of cell advection, in combination with satellite remote sensing, provides early warning of HABs that advect from offshore waters to the coast. Expert interpretation of HAB, biotoxin and environmental data in light of recent and historical trends is used to provide, on a weekly basis, a forecast of the risk from HABs and their biotoxins to allow mitigation measures to be put in place by aquaculture businesses, should a HAB event be imminent