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Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal

Author

Listed:
  • Chimango Nyasulu

    (LANI (Laboratoire d’Analyse Numérique et Informatique), University of Gaston Berger, Saint-Louis 32000, Senegal)

  • Awa Diattara

    (LANI (Laboratoire d’Analyse Numérique et Informatique), University of Gaston Berger, Saint-Louis 32000, Senegal)

  • Assitan Traore

    (Business & Decision, 38000 Grenoble, France)

  • Abdoulaye Deme

    (Laboratoire des Sciences de l’Atmosphère et de l’Océan, Unité de Formation et de Recherche de Sciences Appliquées et de Technologie, University of Gaston Berger, Saint-Louis 32000, Senegal)

  • Cheikh Ba

    (LANI (Laboratoire d’Analyse Numérique et Informatique), University of Gaston Berger, Saint-Louis 32000, Senegal)

Abstract

To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects. Weather information and services are essential for farmers to more effectively survive the increasing occurrence of extreme weather events due to climate change. Weather information is important for resource management in agricultural production and helps farmers plan their farming activities in advance. Machine Learning is one of the technologies used in agriculture for weather forecasting and crop disease detection among others. The objective of this study is to develop Machine Learning-based models adapted to the context of daily weather forecasting for Rainfall, Relative Humidity, and Maximum and Minimum Temperature in Senegal. In this study, we made a comparison of ten Machine Learning Regressors with our Ensemble Model. These models were evaluated based on Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Coefficient of Determination. The results show that the Ensemble Model performs better than the ten base models. The Ensemble Model results for each parameter are as follows; Relative Humidity: Mean Absolute Error was 4.0126, Mean Squared Error was 29.9885, Root Mean Squared Error was 5.4428 and Coefficient of Determination was 0.9335. For Minimum Temperature: Mean Absolute Error was 0.7908, Mean Squared Error was 1.1329, Root Mean Squared Error was 1.0515 and Coefficient of Determination was 0.9018. For Maximum Temperature: Mean Absolute Error was 1.2515, Mean Squared Error was 2.8038, Root Mean Squared Error was 1.6591 and Coefficient of Determination was 0.8205. For Rainfall: Mean Absolute Error was 0.2142, Mean Squared Error was 0.1681, Root Mean Squared Error was 0.4100 and Coefficient of Determination was 0.7733. From the present study, it has been observed that the Ensemble Model is a feasible model to be used for Rainfall, Relative Humidity, and Maximum and Minimum Temperature forecasting.

Suggested Citation

  • Chimango Nyasulu & Awa Diattara & Assitan Traore & Abdoulaye Deme & Cheikh Ba, 2022. "Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal," Agriculture, MDPI, vol. 12(9), pages 1-23, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1473-:d:915212
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    References listed on IDEAS

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    4. Khan, Nasir Abbas & Qiao, Jiamei & Abid, Muhammad & Gao, Qijie, 2021. "Understanding farm-level cognition of and autonomous adaptation to climate variability and associated factors: Evidence from the rice-growing zone of Pakistan," Land Use Policy, Elsevier, vol. 105(C).
    5. Mulungu, Kelvin & Ng'ombe, John, 2019. "Climate Change Impacts on Sustainable Maize Production in Sub-Saharan Africa: A Review," AgriXiv f8pma, Center for Open Science.
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