Dicle University Journal of Engineering, vol.12, no.2, pp.431-438, 2021 (Peer-Reviewed Journal)
Ensuring more reliable and quality meteorological and climatological studies by providing data continuity
and widening the data range. For this reason, missing values in meteorological data such as temperature,
precipitation, evaporation must be completed. In this study, an artificial neural network (ANN) model was
used to complete missing temperature data in the Horasan meteorology station. To establish the ANN
model, monthly average temperature values of neighboring stations having similar climatic characteristics
and altitude with Horasan were used as input. The monthly average temperature values of the Horasan
station were used as output. Approximately 70% of the data was used for training, about 15% for testing,
and about 15% for verification in the ANN model. Various statistical parameters were compared to
determine the best network architecture and best model. As a result, the model's high determination
coefficient (R2 = 0.99) and low mean absolute error (MAE = 0.61) showed that the ANN model can be
used effectively in estimating missing temperature data.