Department of Electronic Engineering, National Chin-Yi University of Technology, 411, Taichung, Taiwan.
Department of Optics and Photonics, National Central University, 320, Taoyuan, Taiwan.
Sci Rep. 2019 Sep 4;9(1):12774. doi: 10.1038/s41598-019-49242-6.
Precipitation is useful information for assessing vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using deep learning algorithms are promising for these purposes. Echo state network (ESN) and Deep Echo state network (DeepESN), referred to as Reservoir Computing (RC), are effective and speedy algorithms to process a large amount of data. In this study, we used the ESN and the DeepESN algorithms to analyze the meteorological hourly data from 2002 to 2014 at the Tainan Observatory in the southern Taiwan. The results show that the correlation coefficient by using the DeepESN was better than that by using the ESN and commercial neuronal network algorithms (Back-propagation network (BPN) and support vector regression (SVR), MATLAB, The MathWorks co.), and the accuracy of predicted rainfall by using the DeepESN can be significantly improved compared with those by using ESN, the BPN and the SVR. In sum, the DeepESN is a trustworthy and good method to predict rainfall; it could be applied to global climate forecasts which need high-volume data processing.
降水对于评估重要水资源、农业、生态系统和水文学具有重要意义。使用深度学习算法进行数据驱动的模型预测，在这些方面具有广阔的应用前景。回声状态网络（ESN）和深度回声状态网络（DeepESN），即所谓的储层计算（RC），是处理大量数据的有效且快速的算法。本研究使用 ESN 和 DeepESN 算法，分析了台湾南部台南观测站 2002 年至 2014 年的气象每小时数据。结果表明，使用 DeepESN 的相关系数优于 ESN 和商业神经网络算法（反向传播网络（BPN）和支持向量回归（SVR），MATLAB，The MathWorks 公司），使用 DeepESN 预测降雨量的准确性也明显优于 ESN、BPN 和 SVR。总的来说，DeepESN 是一种可靠的、优秀的降雨预测方法；它可以应用于需要处理大量数据的全球气候预测。
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