K. I. Chronopoulos

An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations

In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located in southern Greece. Four meteorological stations were established in the canyon; the models were then applied to estimate air temperature values as a function of the corresponding values of one or more reference stations. The evaluation of the ANN model results showed that fair to very good air temperature estimations may be achieved depending on the number of the meteorological stations used as reference stations. In addition, the ANN model was found to have better performance than the MLR model: mean absolute error values were found to be in the range 0.82–1.72°C and 0.90–1.81°C, for the ANN and the MLR models, respectively. These results indicate that ANN models may provide advantages over more traditional models or methods for temperature and other data estimations in areas where meteorological stations are sparse; they may be adopted, therefore, as an important component in various environmental modeling and management studies.

Estimating airborne pollutant concentrations in vegetated urban sites using statistical models with microclimate and urban geometry parameters as predictor variables: A case study in the city of Athens Greece

The present study demonstrates the efficiency of applying statistical models to estimate airborne pollutant concentrations in urban vegetation by using as predictor variables readily available or easily accessible data. Results revealed that airborne cadmium concentrations in vegetation showed a predictable response to wind conditions and to various urban landscape features such as the distance between the vegetation and the adjacent street, the mean height of the adjacent buildings, the mean density of vegetation between vegetation and the adjacent street and the mean height of vegetation. An artificial neural network (ANN) model was found to have superiority in terms of accuracy with an R2 value on the order of 0.9. The lowest R2 value (on the order of 0.7) was associated with the linear model (SMLR model). The linear model with interactions (SMLRI model) and the tree regression (RTM) model gave similar results in terms of accuracy with R2 values on the order of 0.8. The improvement of the results with the use of the non-linear models (RTM and ANN) and the inclusion of interaction terms in the SMLRI model implied the nonlinear relationships of pollutant concentrations to the selected predictors and showed the importance of the interactions between the various predictor variables. Despite the limitations of the models, some of them appear to be promising alternatives to multimedia-based simulation modeling approaches in urban areas with vegetation, where the application of typical deposition models is sometimes limited.

An experimental study of soil temperature regimes associated with solar disinfestation techniques under greenhouse conditions in Greece.

This paper deals with an experimental study of various techniques that have been applied for soil disinfestation purposes under greenhouse conditions. Various meteorological parameters and soil temperatures were measured for four different experimental soil segments (three associated with different disinfestation techniques and one as a reference) at depths varying between 0–1 m and with a time interval of 5 min in a greenhouse located in the Agricultural University of Athens Campus, Greece. Results showed that plastic polyethylene films such as covers, metallic conductors or a combination of both were able to enhance heat transfer and temperature increase in greenhouse soil. For typical disinfestation conditions, the depth-averaged temperature values for plastic covers, metallic conductors, and the combination of both were found to be higher than those for the reference of about 5°C, 12°C and 15°C, respectively. Moreover, the remained population percentages 50 days after the initiation of the experiment were found to be 19.3%, 25.3%, 37.3% κ α ι 94% of the initial population, for the combination of metallic conductors and plastic covers, metallic conductors, plastic cover, and for the reference, respectively.

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

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. Current World Environment Journal Website: www.cwejournal.org ISSN: 0973-4929, Vol. 12, No. (3) 2017, Pg. 547-552 CONTACT Kostas Chronopoulos kchrono@aua.gr Department of Biotechnology, Agricultural University of Athens, Iera Odos 75, Athens 11855, Greece.