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Neural Network for Sky Darkness Level Prediction in Rural Areas

Author

Listed:
  • Alejandro Martínez-Martín

    (Department of Applied Physic, School of Industrial Engineering, University of Extremadura, Avda. de Elvas, S/N, 06006 Badajoz, Spain)

  • Miguel Ángel Jaramillo-Morán

    (Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. de Elvas, S/N, 06006 Badajoz, Spain)

  • Diego Carmona-Fernández

    (Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. de Elvas, S/N, 06006 Badajoz, Spain)

  • Manuel Calderón-Godoy

    (Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. de Elvas, S/N, 06006 Badajoz, Spain)

  • Juan Félix González González

    (Department of Applied Physic, School of Industrial Engineering, University of Extremadura, Avda. de Elvas, S/N, 06006 Badajoz, Spain)

Abstract

A neural network was developed using the Multilayer Perceptron (MLP) model to predict the darkness value of the night sky in rural areas. For data collection, a photometer was placed in three different rural locations in the province of Cáceres, Spain, recording darkness values over a period of 23 months. The recorded data were processed, debugged, and used as a training set (75%) and validation set (25%) in the development of an MLP capable of predicting the darkness level for a given date. The network had a single hidden layer of 10 neurons and hyperbolic activation function, obtaining a coefficient of determination (R 2 ) of 0.85 and a mean absolute percentage error (MAPE) of 6.8%. The developed model could be employed in unpopulated rural areas for the promotion of sustainable astronomical tourism.

Suggested Citation

  • Alejandro Martínez-Martín & Miguel Ángel Jaramillo-Morán & Diego Carmona-Fernández & Manuel Calderón-Godoy & Juan Félix González González, 2024. "Neural Network for Sky Darkness Level Prediction in Rural Areas," Sustainability, MDPI, vol. 16(17), pages 1-13, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7795-:d:1473219
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    References listed on IDEAS

    as
    1. Alejandro Martínez-Martín & Adrián Bocho-Roas & Diego Carmona-Fernández & Manuel Calderón-Godoy & Miguel Ángel Jaramillo-Morán & Juan Félix González, 2023. "Interference of Meteorological Variables on Night Sky Observation in Rural and Urban Zones of South-Western Spain," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    2. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    3. Alejandro Martínez-Martín & Adrián Bocho-Roas & Diego Carmona-Fernández & Manuel Calderón-Godoy & Miguel Ángel Jaramillo-Morán & Juan Félix González, 2023. "Influence of Illumination Parameters on Night Sky Observation in Rural Areas," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    4. Laeticia Jacobs & Elizabeth A. Du Preez & Felicité Fairer-Wessels, 2020. "To wish upon a star: Exploring Astro Tourism as vehicle for sustainable rural development," Development Southern Africa, Taylor & Francis Journals, vol. 37(1), pages 87-104, January.
    5. Amanda Davies, 2021. "COVID-19 and ICT-Supported Remote Working: Opportunities for Rural Economies," World, MDPI, vol. 2(1), pages 1-14, March.
    Full references (including those not matched with items on IDEAS)

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