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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.
Trang Tran; Taesam Lee; Jong-Suk Kim. Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series? Atmosphere 2020, 11, 1072 .
AMA StyleTrang Tran, Taesam Lee, Jong-Suk Kim. Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series? Atmosphere. 2020; 11 (10):1072.
Chicago/Turabian StyleTrang Tran; Taesam Lee; Jong-Suk Kim. 2020. "Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?" Atmosphere 11, no. 10: 1072.
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).
Trang Thi Kieu Tran; Taesam Lee; Ju-Young Shin; Jong-Suk Kim; Mohamad Kamruzzaman. Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization. Atmosphere 2020, 11, 487 .
AMA StyleTrang Thi Kieu Tran, Taesam Lee, Ju-Young Shin, Jong-Suk Kim, Mohamad Kamruzzaman. Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization. Atmosphere. 2020; 11 (5):487.
Chicago/Turabian StyleTrang Thi Kieu Tran; Taesam Lee; Ju-Young Shin; Jong-Suk Kim; Mohamad Kamruzzaman. 2020. "Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization." Atmosphere 11, no. 5: 487.
Trang Thi Kieu Tran; Taesam Lee. Is Deep Better in Extreme Temperature Forecasting? Journal of Korean Society of Hazard Mitigation 2019, 19, 55 -62.
AMA StyleTrang Thi Kieu Tran, Taesam Lee. Is Deep Better in Extreme Temperature Forecasting? Journal of Korean Society of Hazard Mitigation. 2019; 19 (7):55-62.
Chicago/Turabian StyleTrang Thi Kieu Tran; Taesam Lee. 2019. "Is Deep Better in Extreme Temperature Forecasting?" Journal of Korean Society of Hazard Mitigation 19, no. 7: 55-62.