IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v482y2023ics0304380023001461.html
   My bibliography  Save this article

Parametric versus nonparametric machine learning modelling for conditional density estimation of natural events: Application to harmful algal blooms

Author

Listed:
  • Ratté-Fortin, Claudie
  • Plante, Jean-François
  • Rousseau, Alain N.
  • Chokmani, Karem

Abstract

Besides the complex effect of global warming on extreme events, spatiotemporal variability of natural phenomena often carries the legacy of anthropogenic activities. Moreover, any feedback induced by these activities on climate brings additional complexity when modelling natural events. For extreme values, climate or physiographic patterns often induce non stationarity, or long-term changes. In this context, parametric models may become inadequate given the complexity of the studied phenomena and their systematic changes through space and time. In this paper, we assess the use and ensuing efficiency of nonparametric machine learning (npML) methods to estimate and predict extreme values associated with natural events. These npML methods are compared to a commonly used parametric machine learning (pML) approach, the nonstationary frequency analysis model. We use a historical database compiling the frequency of harmful algal blooms (HAB) in Québec, Canada. Results show that a 19-covariate RFCDE model leads to the best mean estimate among the considered models. However, for low and large quantiles, the 4-covariate RCDE model provides better agreement between observed and simulated bloom frequencies. The models may be used to assess the effects of climate change and anthropogenic developments on the frequency of HAB. They may also be leveraged to measure the efficiency of mitigation scenarios and to identify priority areas for restoration plan strategies. Recommendations are finally made regarding the estimation of the conditional density to predict extreme values associated with natural events.

Suggested Citation

  • Ratté-Fortin, Claudie & Plante, Jean-François & Rousseau, Alain N. & Chokmani, Karem, 2023. "Parametric versus nonparametric machine learning modelling for conditional density estimation of natural events: Application to harmful algal blooms," Ecological Modelling, Elsevier, vol. 482(C).
    • Handle: RePEc:eee:ecomod:v:482:y:2023:i:c:s0304380023001461
    DOI: 10.1016/j.ecolmodel.2023.110415
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023001461
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110415?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ratté-Fortin, Claudie & Chokmani, Karem & El Alem, Anas & Laurion, Isabelle, 2022. "A regional model to predict the occurrence of natural events: Application to phytoplankton blooms in continental waterbodies," Ecological Modelling, Elsevier, vol. 473(C).
    2. Jeff C. Ho & Anna M. Michalak & Nima Pahlevan, 2019. "Widespread global increase in intense lake phytoplankton blooms since the 1980s," Nature, Nature, vol. 574(7780), pages 667-670, October.
    3. Martin Edwards & Anthony J. Richardson, 2004. "Impact of climate change on marine pelagic phenology and trophic mismatch," Nature, Nature, vol. 430(7002), pages 881-884, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bo Qin & Rong Wang & Xiangdong Yang & Qinghui Zhang & Jianan Zheng, 2023. "Reconstruction and Trends of Total Phosphorus in Shallow Lakes in Eastern China in The Past Century," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    2. repec:ags:aaea22:335506 is not listed on IDEAS
    3. Alexander E. Cagle & Alona Armstrong & Giles Exley & Steven M. Grodsky & Jordan Macknick & John Sherwin & Rebecca R. Hernandez, 2020. "The Land Sparing, Water Surface Use Efficiency, and Water Surface Transformation of Floating Photovoltaic Solar Energy Installations," Sustainability, MDPI, vol. 12(19), pages 1-22, October.
    4. Aparicio, Genoveva & Camacho, Maximo & Maté-Sánchez-Val, Mariluz, 2024. "Quantifying the impact: Are coastal areas impoverished by marine pollution?," Ecological Economics, Elsevier, vol. 221(C).
    5. Changchun Peng & Zhijun Xie & Xing Jin, 2024. "Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    6. Marco Archetti & Andrew D Richardson & John O'Keefe & Nicolas Delpierre, 2013. "Predicting Climate Change Impacts on the Amount and Duration of Autumn Colors in a New England Forest," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-8, March.
    7. Yang Qu & Qianlai Zhuang, 2020. "Evapotranspiration in North America: implications for water resources in a changing climate," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(2), pages 205-220, February.
    8. Konstantinos Metaxoglou & Aaron Smith, 2022. "Nutrient Pollution and US Agriculture: Causal Effects, Integrated Assessment, and Implications of Climate Change," NBER Chapters, in: American Agriculture, Water Resources, and Climate Change, pages 297-341, National Bureau of Economic Research, Inc.
    9. Ratté-Fortin, Claudie & Chokmani, Karem & El Alem, Anas & Laurion, Isabelle, 2022. "A regional model to predict the occurrence of natural events: Application to phytoplankton blooms in continental waterbodies," Ecological Modelling, Elsevier, vol. 473(C).
    10. Anderson, James J. & Gurarie, Eliezer & Bracis, Chloe & Burke, Brian J. & Laidre, Kristin L., 2013. "Modeling climate change impacts on phenology and population dynamics of migratory marine species," Ecological Modelling, Elsevier, vol. 264(C), pages 83-97.
    11. Liao, Tiancai, 2022. "The impact of plankton body size on phytoplankton-zooplankton dynamics in the absence and presence of stochastic environmental fluctuation," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    12. Scharfe, Mirco & Wiltshire, Karen Helen, 2019. "Modeling of intra-annual abundance distributions: Constancy and variation in the phenology of marine phytoplankton species over five decades at Helgoland Roads (North Sea)," Ecological Modelling, Elsevier, vol. 404(C), pages 46-60.
    13. Koslow, J. Anthony & Couture, Jennifer, 2015. "Pacific Ocean observation programs: Gaps in ecological time series," Marine Policy, Elsevier, vol. 51(C), pages 408-414.
    14. Yongguang Jiang & Peng Xiao & Gongliang Yu & Gaofei Song & Renhui Li, 2020. "Revealing Cryptic Changes of Cyanobacterial Community Structure in Two Eutrophic Lakes Using eDNA Sequencing," IJERPH, MDPI, vol. 17(17), pages 1-14, September.
    15. Chaalali, Aurélie & Beaugrand, Grégory & Raybaud, Virginie & Lassalle, Géraldine & Saint-Béat, Blanche & Le Loc’h, François & Bopp, Laurent & Tecchio, Samuele & Safi, Georges & Chifflet, Marina & Lobr, 2016. "From species distributions to ecosystem structure and function: A methodological perspective," Ecological Modelling, Elsevier, vol. 334(C), pages 78-90.
    16. Kevin C. Rose & Britta Bierwagen & Scott D. Bridgham & Daren M. Carlisle & Charles P. Hawkins & N. LeRoy Poff & Jordan S. Read & Jason R. Rohr & Jasmine E. Saros & Craig E. Williamson, 2023. "Indicators of the effects of climate change on freshwater ecosystems," Climatic Change, Springer, vol. 176(3), pages 1-20, March.
    17. Jingtai Li & Yao Liu & Siying Xie & Min Li & Li Chen & Cuiling Wu & Dandan Yan & Zhaoqing Luan, 2022. "Landsat-Satellite-Based Analysis of Long-Term Temporal Spatial Dynamics of Cyanobacterial Blooms: A Case Study in Taihu Lake," Land, MDPI, vol. 11(12), pages 1-19, December.
    18. R. Iestyn Woolway & Yan Tong & Lian Feng & Gang Zhao & Dieu Anh Dinh & Haoran Shi & Yunlin Zhang & Kun Shi, 2024. "Multivariate extremes in lakes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    19. R. Iestyn Woolway, 2023. "The pace of shifting seasons in lakes," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    20. Mengyao Wu & Hailong Yan & Songhan Fu & Xiaxian Han & Mengzhao Jia & Miaomiao Dou & Han Liu & Nicola Fohrer & Beata Messyasz & Yuying Li, 2025. "Seasonal Dynamics of Planktonic Algae in the Danjiangkou Reservoir: Nutrient Fluctuations and Ecological Implications," Sustainability, MDPI, vol. 17(2), pages 1-19, January.
    21. Xinyan Mao & Xinyu Guo & Yucheng Wang & Katsumi Takayama, 2019. "Influences of Global Warming on the Larval Survival and Transport of Snow Crab ( Chionoecetes opilio ) in the Sea of Japan," Sustainability, MDPI, vol. 11(8), pages 1-17, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:482:y:2023:i:c:s0304380023001461. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.