Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches

Abstract

Because hurricanes, droughts, floods, and heat waves are all important factors in measuring environmental changes, they can all result from changes in atmospheric air temperature and relative humidity (RH). Besides, climate, weather, industry, human health, and plant growth are all affected by RH. Accurately and consistently forecasting RH is a challenge due to its non-linear nature. The present study tried to predict one day ahead of RH in determined provinces from different climatic regions of Turkey (Ankara, Erzurum, Samsun, Diyarbakır, Antalya, and Bilecik) using long short-term memory (LSTM) and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM)–based machine learning models. As evaluation criteria, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were employed. The outcomes from the forecasting models were also validated using observed data. During the testing stage, the smallest MAE and RMSE values were discovered to be 5.76% and 7.51%, respectively, in Erzurum province, with an R value of 0.892 when using the LSTM method. Moreover, the smallest MAE and RMSE values were obtained to be 5.95% and 7.67%, respectively, in Erzurum province with an R value of 0.887 using the ANFIS model according to the daily RH prediction. The results indicate that both the LSTM and ANFIS approaches performed satisfactory performance in daily RH prediction, with the LSTM and ANFIS approaches producing nearly identical results.