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Descriptive and prediction models of phytoplankton in the northern Adriatic

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  • Volf, Goran
  • Atanasova, Nataša
  • Kompare, Boris
  • Precali, Robert
  • Ožanić, Nevenka

Abstract

The northern Adriatic is one of the most productive parts of the Mediterranean Sea due to the nutrient discharges of the Po River. The northwestern part of the northern Adriatic exhibits eutrophic to mesotrophic characteristics with recurrent algal blooms. To contribute to the understanding of eutrophication trends in the northern Adriatic a machine learning tool for induction of models in form of regression trees and in form of set of rules was applied on a data set comprising physical, chemical and biological parameters measured at six stations on the profile from the Po River delta (Italy) to the city of Rovinj on the western coast of Istria (Croatia). Two types of models were successfully elaborated, i.e. (1) the descriptive model to explain the dynamics of phytoplankton concentration in the northern Adriatic as a result of independent environmental variables, and (2) the predictive model to predict the phytoplankton concentration. The descriptive model for phytoplankton dynamics integrates and presents in a user-friendly way the knowledge collected through measurements over a period of 35 years. Such presentation contributes to a better understanding of the ecosystem functioning. The predictive model for phytoplankton concentration successfully forecasts the phytoplankton concentration 14 days in advance.

Suggested Citation

  • Volf, Goran & Atanasova, Nataša & Kompare, Boris & Precali, Robert & Ožanić, Nevenka, 2011. "Descriptive and prediction models of phytoplankton in the northern Adriatic," Ecological Modelling, Elsevier, vol. 222(14), pages 2502-2511.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:14:p:2502-2511
    DOI: 10.1016/j.ecolmodel.2011.02.013
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    Cited by:

    1. Liu, Zhi-bin & Liu, Shu-tang & Tian, Da-dong & Wang, Da, 2021. "Stability analysis of the plankton community with advection," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    2. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    3. Kadukothanahally Nagaraju Shivaprakash & Niraj Swami & Sagar Mysorekar & Roshni Arora & Aditya Gangadharan & Karishma Vohra & Madegowda Jadeyegowda & Joseph M. Kiesecker, 2022. "Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    4. Goran Volf & Ivana Sušanj Čule & Elvis Žic & Sonja Zorko, 2022. "Water Quality Index Prediction for Improvement of Treatment Processes on Drinking Water Treatment Plant," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    5. Shen, Jian & Qin, Qubin & Wang, Ya & Sisson, Mac, 2019. "A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading," Ecological Modelling, Elsevier, vol. 398(C), pages 44-54.

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