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Human thermal sensation over a mountainous area, revealed by the application of ANNs: the case of Ainos Mt., Kefalonia Island, Greece

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Abstract

Mt. Ainos in Kefalonia Island, Greece, hosts a large variety of plant species, some of them endemic to the region. Because of its rich biodiversity, a large portion of the mountain area is designated as National Park and is protected from human activities such as hunting or logging. Therefore, the area presents a lot of opportunities for ecotourist activities, such as trekking, birdwatching, and mountain climbing. In order to estimate its touristic activities potential, it is essential to assess the mountain’s biometeorological conditions. To achieve that, the human thermal index PET (physiologically equivalent temperature) was used, which is based on a human energy balance model. However, it is difficult to get the specific meteorological data over mountainous areas (air temperature, humidity, wind speed, and global solar radiation), appropriate as input variables for PET modeling. In order to overcome this limitation, artificial neural networks (ANNs) were developed for the estimation of PET index in ten sites within the Ainos National Park. In the process, the spatiotemporal distributions of the PET thermal index were illustrated, taking into consideration the ANN modeling. The findings of the performed analysis shed light that Mt. Ainos offers the greatest touristic opportunities from May to September, when thermal comfort conditions appear. The study also proves that the highest frequency of thermal comfort appears within the aforementioned time period over the highest altitudes, while on the contrary, slightly warm class appears as the altitude decreases on both sides of the mountain.

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Authors and Affiliations

  1. Faculty of Crop Science, Laboratory General & Agricultural Meteorology, Agricultural University of Athens, 75 Iera Odos, 11855, Athens, Greece

    Stelios Maniatis & Athanasios Kamoutsis

  2. Laboratory of Climatology and Atmospheric Environment, National and Kapodistrian University of Athens, University Campus, 15784, Athens, Greece

    Panagiotis T. Nastos & Iliana D. Polychroni

  3. Department of Mechanical Engineering, University of West Attica, Campus 2, 250 Thivon & P. Ralli, 12244, Aigaleo, Greece

    Kostas Moustris

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  1. Stelios Maniatis
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  2. Panagiotis T. Nastos
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  3. Kostas Moustris
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  4. Iliana D. Polychroni
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  5. Athanasios Kamoutsis
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Correspondence to Panagiotis T. Nastos.

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Appendix

Appendix

  1. 1.

    Specifications of the sensors and data loggers, used in the ten sites over Ainos Mt., Kefalonia, Greece:

    • Air temperature sensor accuracy: ± 0.25 °C from − 40 to 0 °C, ± 0.2 °C from 0 to 70 °C; relative humidity sensor accuracy: ± 2.5% from 10 to 90% and ± 2.5 to ± 4.0% from 90 to 100%).

    • Data logger: Hobo Pro v2 U23-001, Onset Computer Corporation, USA

  1. 2.

    Specifications of the sensors and data loggers, used in the reference station at Eudoxos National Astronomy Center (38° 10′ 05.1″ Ν, 20° 36′ 59.6″ Ε), located near the Ainos National Park:

    • Air temperature and relative humidity sensors: Rotronic, T & D Thorne & Derick International sensor (accuracy ± 0.8% for relative humidity and ± 0.1 K at 10–30 °C for air temperature).

    • Wind speed sensor: Adolf Thies GmbH and Co K.G. anemometer wind transmitter (resolution 0.05 m wind run).

    • Data logger: CR200, Campbell Scientific Inc.

    • Global solar radiation sensor: Pyranometer: PYRA 08 AV, Delta Ohm, typical sensitivity 10mv, spectral field from 305 to 2800 nm

    • Data logger: Stylitis-10, Symmetron Electronic Applications

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Maniatis, S., Nastos, P.T., Moustris, K. et al. Human thermal sensation over a mountainous area, revealed by the application of ANNs: the case of Ainos Mt., Kefalonia Island, Greece. Int J Biometeorol 64, 2033–2045 (2020). https://doi.org/10.1007/s00484-020-01993-y

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  • DOI: https://doi.org/10.1007/s00484-020-01993-y

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