A Machine Learning approach to improve the monitoring of Sustainable Development Goals : a case study in Senegalese artisanal fisheries - Archive ouverte HAL
Conference Papers Year : 2021

A Machine Learning approach to improve the monitoring of Sustainable Development Goals : a case study in Senegalese artisanal fisheries

Abstract

Since the adoption of the Sustainable Development Goals (SDGs), the international community’s efforts to achieve the SDGs have been unevenly distributed. An exploration of the data related to these goals raises the urgent need to mo- nitor them in order to better focus efforts in the least develo- ped countries. We propose here a method based on Machine Learning to overcome the lack of data and the need for dynamic monitoring. This method is based on three prin- ciples : participatory research, context localized process and dynamic model development. An illustrative example is presented in the context of the development of new data sets and prediction models in Senegal, showing the interest of the proposed approach.
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Dates and versions

hal-03319136 , version 1 (11-08-2021)

Identifiers

  • HAL Id : hal-03319136 , version 1

Cite

Théophile Bayet, Timothée Brochier, Christophe Cambier, Alassane Bah, Christophe Denis, et al.. A Machine Learning approach to improve the monitoring of Sustainable Development Goals : a case study in Senegalese artisanal fisheries. CNIA 2021 : Conférence Nationale en Intelligence Artificielle, Jun 2021, Bordeaux (virtuel), France. pp.30-37. ⟨hal-03319136⟩
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