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        <title type="main" level="a">Citizen science and machine learning forecasting Ostreopsis cf ovata blooms.</title>
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          <persName n="1" ref="https://orcid.org/0000-0001-5777-3651" type="ORCID">
            <forename>Maddalena</forename>
            <surname>de Virgilio</surname>
            <placeName type="affiliation">CNR-IBBR, Institute of Bioscience and Bioresources, Italy</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0009-0004-6217-5786" type="ORCID">
            <forename>Patrizia</forename>
            <surname>Borrello</surname>
            <placeName type="affiliation">ISPRA - Italian Institute for Environmental Protection and Research, Italy</placeName>
          </persName>
          <persName n="3" ref="https://orcid.org/0009-0002-1494-5171" type="ORCID">
            <forename>Emanuela</forename>
            <surname>Spada</surname>
            <placeName type="affiliation">ISPRA - Italian Institute for Environmental Protection and Research, Italy</placeName>
          </persName>
          <persName n="4">
            <forename>Pasquale</forename>
            <surname>Cataldo</surname>
            <placeName type="affiliation">CNR-IBBR, Institute of Bioscience and Bioresources, Italy</placeName>
          </persName>
          <persName n="5">
            <forename>Salvatore</forename>
            <surname>Cifarelli</surname>
            <placeName type="affiliation">Osservatorio del Mare a Molfetta, Italy</placeName>
          </persName>
          <persName n="6">
            <forename>Giuseppe</forename>
            <surname>Garofoli</surname>
            <placeName type="affiliation">Osservatorio del Mare a Molfetta, Italy</placeName>
          </persName>
          <persName n="7">
            <forename>Grazia</forename>
            <surname>Lamberti</surname>
            <placeName type="affiliation">Osservatorio del Mare a Molfetta, Italy</placeName>
          </persName>
          <persName n="8">
            <forename>Bernard</forename>
            <surname>Degryse</surname>
            <placeName type="affiliation">Osservatorio del Mare a Molfetta, Italy</placeName>
          </persName>
          <persName n="9" ref="https://orcid.org/0000-0002-2538-5985" type="ORCID">
            <forename>Ennio</forename>
            <surname>Ottaviani</surname>
            <placeName type="affiliation">University of Genoa, Italy</placeName>
          </persName>
        </author>
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          <resp>This is a section of <title>Tenth InternationaSymposium Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques</title>(DOI: <idno type="DOI">10.36253/979-12-215-0556-6</idno>) by </resp>
          <name>Laura Bonora, Marcantonio Catelani, Matteo De Vincenzi, Giorgio Matteucci</name>
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        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2024">2024</date>
        <idno type="DOI">https://doi.org/10.36253/979-12-215-0556-6.76</idno>
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          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
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            <p>Content licence CC BY-NC-SA 4.0</p>
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        <p>This is original content, published for academic research purposes</p>
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      <abstract xml:lang="en">
        <p>The toxic benthic dinoflagellate Ostreopsis cf. ovata causes harmful algal blooms in Apulia region of Southern Italy. In this study the volunteers of a citizens’ observatory engaged with public research centres to apply a machine learning approach and develop a predictive modelling tool able to forecast O. ovata blooms. We applied the Quantile Regression Forest to draw up two models named Model4Cities and Citizens’Model. Model4Cities was trained with data of cell abundance detected by the Regional Agency of Environmental Protection of Apulia from 2010 to 2022 in the cities of Bisceglie, Molfetta, Giovinazzo and Bari where the microalgae proliferate at high rates. Citizen’sModel was trained with data of cell abundance detected by citizens in two sites within the coastline of Molfetta from 2016 to 2022. Both models show a good capacity to forecast O. ovata concentrations as function of meteorological open data with 81% and 89% of prediction accuracy.</p>
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        <keywords>
          <list>
            <item>Ostreopsis ovata</item>
            <item>Forecast</item>
            <item>Citizen science</item>
            <item>Machine learning</item>
            <item>Apulia Region</item>
          </list>
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      <p>It is available online at https://doi.org/10.36253/979-12-215-0556-6.76<ref target="https://doi.org/10.36253/979-12-215-0556-6.76" /></p>
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