Developing a novel hybrid model based on deep neural networks and discrete wavelet transform algorithm for prediction of daily air temperature

The precise predicting of air temperature has a significant influence in many sectors such as agriculture, industry, modeling environmental processes. In this work, to predict the mean daily time series air temperature in Muğla city (AT_m), Turkey, initially, five different layer structures of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning-based neural network models through the seq2seq regression forecast module are developed. Then, based on performance evaluation metrics, an optimal DL-based layer network structure designed is chosen to hybridize with the wavelet transform (WT) algorithm (i.e., WT-DNN model) to enhance the estimation capability. In this direction, among potential meteorological variables considered, the average daily sunshine duration (SSD) (hours), total global solar radiation (TGSR) (kw. hour/m²), and total global insolation intensity (TGSI) (watt/m²) from Jan 2014 to Dec 2019 are picked as the most effective input variables through correlation analysis to predict AT_m. To thwart overfitting and underfitting problems, different algorithm tuning along with trial-and-error procedures through diverse types of hyper-parameters are performed. Consistent with the performance evaluation standards, comparison plots, and Total Learnable Parameters (TLP) value, the state-of-the-art and unique proposed hybrid WT-(LSTM × GRU) model (i.e., hybrid WT with the coupled version of LSTM and GRU models via Multiplication layer ( \(\times\) $\times$ )) is confirmed as the best model developed. This hybrid model under the ideal hyper-parameters resulted in an R² = 0.94, an RMSE = 0.56 (^°C), an MBE = -0.5 (^°C), AICc = -382.01, and a running time of 376 (s) in 2000 iterations. Nonetheless, the standard single LSTM layer network model as benchmark model resulted in an R² = 0.63, an RMSE = 4.69 (^°C), an MBE = -0.89 (^°C), AICc = 1021.8, and a running time of 186 (s) in 2000 iterations.