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

Abstract

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.

Keywords:

• Artificial neural networks
• estimation
• prediction
• model
• environmental management
• meteorological data
• meteorological stations

Notes

∗For example: the CCF value between S2 and S1 at time x is 0.84, while the CCF value between S2 at time x and S1 3 hours before (i.e., time x−3) is 0.87

∗Correlation significance level 0.01 (2-tailed)