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Statistical Models in Estimating Air Temperature in a Mountainous Region of Greece

Stelios Maniatis1 * , Kostas Chronopoulos2 , Aristidis Matsoukis1 and Athanasios Kamoutsis1

1 Department of Crop Science, Iera Odos 75, Agricultural University of Athens, Athens, 11855 Greece

2 Department of Biotechnology, Iera Odos 75, Agricultural University of Athens, Athens, 11855 Greece

Corresponding author Email: steman78@hotmail.com

DOI: http://dx.doi.org/10.12944/CWE.12.3.07

The current work focuses on the estimation of air temperature (T) conditions in two high altitude (alt) sites (1580 m), each one at different orientation (southeast and northwest) in the mountain (Mt) Aenos in the island of Cephalonia, Greece, by using two well-known statistical models, simple linear regression (SLR) and multi-layer perceptron ( MLP), one of the most commonly used artificial neural networks. More specifically, the estimation of mean, maximum and minimum T in high alt sites was based on the respective T data of two lower alt sites (1100 m), the first at southeast and the second at northwest orientations, and was carried out separately for each orientation. The performance of both SLR and MLP models was evaluated by the coefficient of determination (R2) and the Mean Absolute Error (MAE). Results showed that the examined models (SLR and MLP) provided very satisfactory results with regard to the estimation of mean, maximum and minimum T, regarding southeast orientation (R2 ranging from 0.96 to 0.98), with mean T estimation being relatively better, as confirmed by the lowest MAE (0.83). Regarding northwest orientation, T estimation was less accurate (lower R2 and higher MAE), compared to the respective estimation of southeast orientation, but, the results were considered adequate (R2 and MAE ranging from 0.88 to 0.92 and 1.00 to 1.40, respectively). In general, the estimations of the mean T were better than those of the extreme ones (minimum and maximum T). In addition, better results (higher R2 and lower, in general, MAE) were obtained when T estimations were based on T data derived from sites located at areas with similar surroundings, as in the case of dense and tall vegetation of the sites at southeast orientation, irrespective of applied method.

Aenos mountain; Air temperature; Artificial neural network models; Cephalonia island; Estimation; Greece; Linear regression

Copy the following to cite this article:

Maniatis S, Chronopoulos K, Matsoukis A, Kamoutsis A. Statistical Models in Estimating Air Temperature in a Mountainous Region of Greece. Curr World Environ 2017;12(3). DOI:http://dx.doi.org/10.12944/CWE.12.3.07

Copy the following to cite this URL:

Maniatis S, Chronopoulos K, Matsoukis A, Kamoutsis A. Statistical Models in Estimating Air Temperature in a Mountainous Region of Greece. Curr World Environ 2017;12(3). Available from: http://www.cwejournal.org/?p=1062

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