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An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande

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  • Anwar Musah

    (UCL Department of Geography, Geospatial Analytics and Computing Group (GSAC), University College London, London WC1E 6BT, UK
    UCL Centre for Digital Public Health & Emergencies, University College London, London WC1E 6BT, UK)

  • Livia Màrcia Mosso Dutra

    (Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil)

  • Aisha Aldosery

    (UCL Centre for Digital Public Health & Emergencies, University College London, London WC1E 6BT, UK)

  • Ella Browning

    (Centre for Biodiversity and Environment Research, Department of Genetics, Evolution & Environment, University College London, London WC1E 6BT, UK)

  • Tercio Ambrizzi

    (Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil)

  • Iuri Valerio Graciano Borges

    (Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil)

  • Merve Tunali

    (Institute of Environmental Sciences, Boğaziçi University, Bebek, Istanbul 34342, Turkey)

  • Selma Başibüyük

    (Institute of Environmental Sciences, Boğaziçi University, Bebek, Istanbul 34342, Turkey)

  • Orhan Yenigün

    (Institute of Environmental Sciences, Boğaziçi University, Bebek, Istanbul 34342, Turkey
    School of Engineering, European University of Lefke, Lefke 99010, Northern Cyprus, Turkey)

  • Giselle Machado Magalhaes Moreno

    (Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG), University of São Paulo, São Paulo 05508-010, Brazil)

  • Ana Clara Gomes da Silva

    (Department of Biomedical Engineering, Federal University of Pernambuco, Recife-PE 50740-550, Brazil)

  • Wellington Pinheiro dos Santos

    (Department of Biomedical Engineering, Federal University of Pernambuco, Recife-PE 50740-550, Brazil)

  • Clarisse Lins de Lima

    (Polytechnic School of Pernambuco, University of Pernambuco (Poli-UPE), Recife-PE 50720-001, Brazil)

  • Tiago Massoni

    (Department Systems & Computing, Federal University of Campina Grande, Campina Grande-PB 58429-900, Brazil)

  • Kate Elizabeth Jones

    (Centre for Biodiversity and Environment Research, Department of Genetics, Evolution & Environment, University College London, London WC1E 6BT, UK)

  • Luiza Cintra Campos

    (Department of Civil, Environmental & Geomatic Engineering, University College London, London WC1E 6BT, UK)

  • Patty Kostkova

    (UCL Centre for Digital Public Health & Emergencies, University College London, London WC1E 6BT, UK)

Abstract

Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online open-source weather platforms that provide historical, current and future weather forecasts which can be utilised for general predictions, and these electronic sources serve as an alternate option for weather data when physical weather stations are inaccessible (or inactive). Before using data from such online source, it is important to assess the accuracy against some baseline measure. In this paper, we therefore evaluated the accuracy and suitability of weather forecasts of two parameters namely temperature and humidity from the OpenWeatherMap API (an online weather platform) and compared them with actual measurements collected from the Brazilian weather stations (INMET). The evaluation was focused on two Brazilian cites, namely, Recife and Campina Grande. The intention is to prepare an early warning model which will harness data from OpenWeatherMap API for mosquito prediction.

Suggested Citation

  • Anwar Musah & Livia Màrcia Mosso Dutra & Aisha Aldosery & Ella Browning & Tercio Ambrizzi & Iuri Valerio Graciano Borges & Merve Tunali & Selma Başibüyük & Orhan Yenigün & Giselle Machado Magalhaes Mo, 2022. "An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande," Data, MDPI, vol. 7(8), pages 1-13, July.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:8:p:106-:d:876511
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    References listed on IDEAS

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    1. Cailly, Priscilla & Tran, Annelise & Balenghien, Thomas & L’Ambert, Grégory & Toty, Céline & Ezanno, Pauline, 2012. "A climate-driven abundance model to assess mosquito control strategies," Ecological Modelling, Elsevier, vol. 227(C), pages 7-17.
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    1. Marzhan Sadenova & Nail Beisekenov & Petar Sabev Varbanov & Ting Pan, 2023. "Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan," Agriculture, MDPI, vol. 13(6), pages 1-27, June.

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