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Nonparametric Prediction and Supervised Classification for Spatial Dependent Functional Data Under Fixed Sampling Design

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Nonlinear Analysis, Geometry and Applications

Part of the book series: Trends in Mathematics ((TM))

Abstract

Fisheries science has been trying to identify the best way to analyze and predict fish biomass and its spatial distribution since several decades using, among others, kirigng model, co-kiriging model, Species Distribution Modeling and Joined Species Distribution Modeling, based on conventional statistical methods as Generalized Linear Models and Generalized Additive Models, with contested results. We consider a bio-ecological issue applying a non parametric spatial prediction based on a spatio-functional regression models, in a fixed design sampling context, as a supervised classification method when the variable of interest belongs to a predefined class set. The proposed predictor takes into account the spatial fish distribution and environmental variable such as salinity and temperature. The development of the method depends on two kernels to control both interactions between observations and locations. The results show that this nonparametric spatial functional supervised classification method is an efficient tool applied to predict spatial distribution of demersal coastal fish off Senegal.

To all my supervisor and fee supports: Sophie DABO-NIANG, Papa NGOM and Patrice Brehmer.

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Notes

  1. 1.

    Is a medium-sized species of scallop, an edible marine bivalve mollusk in the family Pectinidae, the scallops. It is found in the northeast Atlantic and is important in fisheries.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Cheikh Anta Diop University, Dakar, Senegal

    Mamadou Ndiaye & Papa Ngom

  2. University of Lille, LEM-CNRS 9221, INRIA-MODAL, Villeneuve d’ascq, France

    Sophie Dabo-Niang

  3. Oceanographic Research Center of Dakar-Thiaroye, Senegalese Institute for Agricultural Research (ISRA-CRODT, Dakar, Senegal

    Ndiaga Thiam

  4. IRD Centre de Bretagne, UMR195 Lemar, Plouzané, France

    Patrice Brehmer

  5. Mauritanian Institute for Oceanographic Research and Fisheries, Nouadhibou, Mauritania

    Yeslem El Vally

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  1. Mamadou Ndiaye
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  2. Sophie Dabo-Niang
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  3. Papa Ngom
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  4. Ndiaga Thiam
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  5. Patrice Brehmer
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  6. Yeslem El Vally
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Editor information

Editors and Affiliations

  1. Department of Mathematics of Decision, University Cheikh Anta Diop of Dakar, Dakar, Senegal

    Diaraf Seck

  2. Université Félix H.B. d’Abidjan, Abidjan, Côte d'Ivoire

    Kinvi Kangni

  3. Université Assane Seck de Ziguinchor, Ziguinchor, Senegal

    Marie Salomon Sambou

  4. Laboratoire de Recherche en Mathématiques, École Normale Supérieure, Libreville, Gabon

    Philibert Nang

  5. African Institute for Mathematical Sciences, Mbour, Senegal

    Mouhamed Moustapha Fall

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Ndiaye, M., Dabo-Niang, S., Ngom, P., Thiam, N., Brehmer, P., El Vally, Y. (2024). Nonparametric Prediction and Supervised Classification for Spatial Dependent Functional Data Under Fixed Sampling Design. In: Seck, D., Kangni, K., Sambou, M.S., Nang, P., Fall, M.M. (eds) Nonlinear Analysis, Geometry and Applications. Trends in Mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-031-52681-7_3

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