One-Day Prediction of Biometeorological Conditions in a Mediterranean Urban Environment Using Artificial Neural Networks Modeling | Zendy

Konstantinos Moustris | Zendy; P. T. Nastos | Zendy; Α. Γ. Παλιατσός | Zendy

Open Access

One-Day Prediction of Biometeorological Conditions in a Mediterranean Urban Environment Using Artificial Neural Networks Modeling

Author(s) -

Konstantinos Moustris,

P. T. Nastos,

Α. Γ. Παλιατσός

Publication year - 2013

Publication title -

advances in meteorology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.482

H-Index - 32

eISSN - 1687-9317

pISSN - 1687-9309

DOI - 10.1155/2013/538508

Subject(s) - artificial neural network , geography , extreme value theory , heat stress , thermal sensation , meteorology , environmental science , cartography , climatology , mathematics , statistics , demography , artificial intelligence , computer science , zoology , biology , thermal comfort , geology , sociology

The present study, deals with the 24-hour prognosis of the outdoor biometeorological conditions in an urban monitoring site within the Greater Athens area, Greece. For this purpose, artificial neural networks (ANNs) modelling techniques are applied in order to predict the maximum and the minimum value of the physiologically equivalent temperature (PET) one day ahead as well as the persistence of the hours with extreme human biometeorological conditions. The findings of the analysis showed that extreme heat stress appears to be 10.0% of the examined hours within the warm period of the year, against extreme cold stress for 22.8% of the hours during the cold period of the year. Finally, human thermal comfort sensation accounts for 81.8% of the hours during the year. Concerning the PET prognosis, ANNs have a remarkable forecasting ability to predict the extreme daily PET values one day ahead, as well as the persistence of extreme conditions during the day, at a significant statistical level of

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