Daily air temperature forecasting using LSTM-CNN and GRU-CNN models

Today, air temperature (AT) is the most critical climatic indicator. This indicator accurately defines global warming and climate change, despite the fact that it has effects on different things, including the environment, hydrology, agriculture, and irrigation. Accurate and timely AT forecasting is crucial since it supplies more significant details that can create credibility for future planning. This study proposes innovative hybrid models that integrate a convolutional neural network (CNN) with a long short-term memory (LSTM) neural network and a gated recurrent unit (GRU) to perform one-day ahead AT predictions. For this purpose, the daily AT data obtained from 2012 to 2019 at the Adana and Ankara meteorological stations over Turkiye under Continental and Mediterranean climate conditions are used. The hybrid GRU-CNN and LSTM-CNN models are compared with various traditional statistical and machine-learning models such as feed-forward neural network, adaptive neuro-fuzzy inference system, autoregressive moving average, GRU, CNN, and LSTM. The success of the prediction models is evaluated utilizing various statistical criteria (MAE, RMSE, NSE, and R2) and visual comparisons. The results show that the proposed hybrid GRU-CNN and LSTM-CNN models in one day-ahead AT predictions yield the best results among all models with high accuracy.