Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).

Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by employing various scenarios for the number of parameters and hidden layers in the ANN model. Specifically, a different number of trainable parameters (i.e., the total number of weights and bias) and distinctive numbers of hidden layers were compared for system-performance effects. If the parameter sizes were too large, the root mean square error (RMSE) would be generally increased, and the model’s ability was impaired. Besides, too many hidden layers would reduce the system prediction if the number of parameters was high. The number of parameters and hidden layers affected the performance of ANN models for time series forecasting competitively. The result showed that the five-hidden layer model with 49 parameters produced the smallest RMSE at most South Korean stations.

In recent years, the application of deep learning based on artificial neural networks (ANNs) to forecast highly non-linear and complex weather phenomena, such as rainfall, wind speed, and temperature, has become an attractive pursuit in the field of environmental sciences. However, the critical research addressing the question of whether or not a deep learning network can perform better has not been completed. The current study conducted a systematic comparison of a one-hidden-layer (shallow) network and a multiple-hidden-layer (deep) network in maximum temperature forecasting Datasets of daily maximum temperature at five stations in South Korea, spanning the years 1976 to 2015, were used for training and testing the different-architecture models, respectively. With each model, one-day-ahead forecasting was made for the winter, spring, summer, and autumn seasons. Moreover, the performance and effectiveness of the models were then assessed by the root mean square error (RMSE). In addition, a genetic algorithm was applied to select the optimal network architecture. Finally, the empirical results indicated that the ANN model with one hidden layer, compared with the case of multiple-hidden-layer networks, produced the most accurate forecasts.