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.

中文翻译：

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在预测最高温度时间序列时增加神经元或加深层？

天气预报，特别是极端气候事件的天气预报，由于其对自然生态系统和人类生活的影响，已经引起了研究人员的广泛关注。人工神经网络（ANN）在非线性过程预测中的适用性极大地促进了水文气候学的发展。神经网络功能的效率取决于网络结构和参数。这项研究提出了一种新的预测韩国未来一天最高温度时间序列的方法，该方法通过针对ANN模型中参数和隐藏层的数量采用各种方案来讨论网络规格与性能之间的关系。具体而言，不同数量的可训练参数（即，比较权重和偏差的总数）和隐藏层的独特数量，以获取系统性能效果。如果参数大小太大，则一般会增加均方根误差（RMSE），从而削弱模型的功能。此外，如果参数数量过多，太多的隐藏层将降低系统预测。参数的数量和隐藏层的数量会竞争性地影响神经网络模型的时间序列预测性能。结果表明，在大多数韩国站中，具有49个参数的五隐藏层模型产生的最小RMSE。如果参数数量过多，太多的隐藏层将降低系统预测。参数的数量和隐藏层的数量会竞争性地影响神经网络模型的时间序列预测性能。结果表明，在大多数韩国站中，具有49个参数的五隐藏层模型产生的最小RMSE。如果参数数量过多，太多的隐藏层将降低系统预测。参数的数量和隐藏层的数量会竞争性地影响神经网络模型的时间序列预测性能。结果表明，在大多数韩国站中，具有49个参数的五隐藏层模型产生的最小RMSE。