School of Civil and Environmental Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia.
Comput Intell Neurosci. 2021 Oct 27;2021:5172658. doi: 10.1155/2021/5172658. eCollection 2021.
Reliable and accurate streamflow simulation has a vital role in water resource development, mainly in agriculture, environment, domestic water supply, hydropower generation, flood control, and early warning systems. In this context, these days, deep learning algorithms have got enormous attention due to their high-performance simulation capacity. In this study, we compared multilayer perceptron (MLP), long short-term memory (LSTM), and gated recurrent unit (GRU) with the proposed new hybrid models, including CNN-LSTM and CNN-GRU. Hence, we can simulate one-step daily streamflow in different agroclimatic conditions, rolling time windows, and a range of variable input combinations. The analysis used daily multivariate and multisite time series data collected from Awash River Basin (Borkena watershed: Ethiopia) and Tiber River Basin (Upper Tiber River Basin: Italy) stations. The datasets were subjected to rigorous quality control processes. Consequently, it rolled to a different time lag to remove noise in the time series and further split into training and testing datasets using a ratio of 80 : 20, respectively. Finally, the results showed that integrating the GRU layer with the convolutional layer and using monthly rolled average daily input time series could substantially improve the simulation of streamflow time series.
可靠准确的流量模拟在水资源开发中起着至关重要的作用，主要应用于农业、环境、城市供水、水力发电、防洪和预警系统。在这种背景下，深度学习算法由于其出色的模拟能力，最近受到了极大的关注。在本研究中，我们将多层感知机（MLP）、长短期记忆（LSTM）和门控循环单元（GRU）与提出的新混合模型（包括 CNN-LSTM 和 CNN-GRU）进行了比较。因此，我们可以在不同的农业气候条件、滚动时间窗口和一系列变量输入组合下模拟一步日流量。该分析使用了来自 Awash 河流域（埃塞俄比亚的 Borkena 流域）和 Tiber 河流域（意大利的上 Tiber 河流域）站点的多变量和多站点日时间序列数据。数据集经过了严格的质量控制过程。因此，通过不同的时间滞后滚动来去除时间序列中的噪声，并进一步将其划分为训练集和测试集，比例分别为 80:20。最后，结果表明，将 GRU 层与卷积层集成，并使用月度滚动平均日输入时间序列，可以显著提高流量时间序列的模拟效果。
