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Prediction of Atmospheric Air Temperature Using Long Short-Term Memory (LSTM) Recurrent Neural Network

Atmospheric Air Temperature (AAT) is a very important weather element whose value can change at any time. Prediction of AAT time series is an important issue as it gives information to the society and sustainability for future planning. Thus, predicting AAT with high accuracy is required for reliable decision making in different areas. Recently, the utilization of artificial intelligence methods in predicting has become increasingly popular. Therefore, in this study, a deep learning method, namely, Long Short-Term Memory (LSTM) network model, based on one-step-ahead prediction approach was proposed to predict AAT using the actual observed time series data of AAT. For the proposed prediction method, a set of measurement data in 10-minute and hourly intervals obtained from Mersin Station and Belen Station, respectively, which are located in the Eastern Mediterranean Region of Turkey was used. Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE) and Average Bias were used as evaluation criteria. The predicted AAT values from the LSTM network were compared with the actual observed AAT data in order to define their errors. According to the testing process (prediction phase), the RMSE, MAPE, MAE, R and Bias values for the hourly AAT from Belen station were calculated as 0.61 oC, 1.85%, 0.43 oC, 0.945 and -0.013 oC, respectively. Considering the prediction results of the 10-minute AAT from Mersin station, the above statistical metrics with the same order were obtained as 0.35 oC, 1.40%, 0.25 oC, 0.995 and 0.074 oC. The experimental results from two stations show that the prediction of AATs using LSTM can provide high accuracy results for short-term prediction using data with a long period of time. Thanks to prediction of AATs, decision makers may prepare an action plan in case of an emergency scenario.