Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA

Abstract

Prediction of atmospheric air temperature (AAT) time series is an important issue as it gives information to society and sustainability for future planning. In this study, a deep learning method, namely, long short-term memory (LSTM) network, based on one-step-ahead prediction approach was proposed to predict AAT using the actual time series data. For the proposed prediction method, a set of measurement data in 10-min, hourly, and daily intervals obtained from Mersin and Belen stations 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 considered as evaluation criteria. According to the testing process, the RMSE, MAPE, MAE, R, and bias values for the 10-min interval AAT prediction were calculated as 0.35 °C, 1.40%, 0.25 °C, 0.995, and 0.074 °C, respectively. Considering the prediction results of the hourly AAT predication, the above statistical metrics with the same order were obtained as 0.61 °C, 1.85%, 0.43 °C, 0.945, and −0.013 °C. Concerning the daily AAT prediction results with LSTM, the above statistical metrics with the same order were computed as 1.33 °C, 3.27%, 0.99 °C, 0.97, and −0.116 °C. Compared to the hourly and daily AAT predictions, LSTM provided better accuracy results in predicting 10-min interval AAT. The prediction results from the three different time series data show that the prediction of AATs using LSTM can provide high accuracy results for short-term prediction using data with a long period time. On the other hand, adaptive neuro-fuzzy inference system with fuzzy C-means (ANFIS-FCM) method and autoregressive moving average (ARMA) model were also used to compare the results of LSTM method. Both LSTM and ANFIS-FCM network model showed high accuracy for the prediction of 10-min interval, hourly, and daily AAT data with RMSE values between 0.31 and 1.52 °C, while ARMA model failed to provide high accuracies for all predictions.