Farhat Abdul Wajed, Deepthi B, Sivakumar Bellie
Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.
Entropy (Basel). 2024 Jul 18;26(7):609. doi: 10.3390/e26070609.
The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.
可靠的径流预测对于各种水资源、环境和生态系统应用至关重要。当前的研究采用基于复杂网络的方法来预测径流。该方法包括三个主要步骤：（1）使用径流时间序列形成网络；（2）计算聚类系数（CC）作为网络度量；（3）使用基于聚类系数的最近邻搜索程序进行径流预测。对于网络构建，每个时间步长被视为一个节点，并且基于节点径流值之间的差异（距离）来确定任意节点对之间链接的存在。使用不同的距离阈值来确定形成网络的临界距离阈值。基于复杂网络的方法被用于预测美国本土142个站点的日径流。使用三种统计量来量化预测精度：相关系数（R）、归一化均方根误差（NRMSE）和纳什-萨特克利夫效率（NSE）。还研究了邻居数量对预测精度的影响。在临界距离阈值下获得的结果表明，142个站点的聚类系数范围为0.799至0.999。总体而言，对于所有142个站点，该预测方法都产生了相当不错的结果，R值范围为0.05至0.99，NRMSE值范围为0.1至12.3，NSE值范围为 -0.89至0.99。还尝试研究预测精度与流域特征/径流统计特性（流域面积、平均流量、流量变异系数）之间的关系。结果表明，预测精度与流域面积和平均径流值关系不大，而与流量变异系数有关。这项研究的结果对于基于复杂网络的概念在径流（以及其他水文）时间序列预测中的应用肯定是有前景的。
