Khan Shakera K, Sivakumar Bellie
Water Forecasting Team, Environmental Prediction Services Program, Bureau of Meteorology, Sydney, NSW 2010, Australia.
Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.
Entropy (Basel). 2024 Feb 29;26(3):218. doi: 10.3390/e26030218.
Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation prediction method. The method uses the concept of phase-space reconstruction of a time series to represent the underlying system dynamics and identifies nearest neighbors in the phase space for system evolution and prediction. The prediction accuracy measures, as well as the optimum values of the parameters involved in the method (e.g., phase space or embedding dimension, number of neighbors), are used for classification. For implementation, the method is applied to daily streamflow data from 218 catchments in Australia, and predictions are made for different embedding dimensions and number of neighbors. The prediction results suggest that phase-space reconstruction using streamflow alone can provide good predictions. The results also indicate that better predictions are achieved for lower embedding dimensions and smaller numbers of neighbors, suggesting possible low dimensionality of the streamflow dynamics. The classification results based on prediction accuracy are found to be useful for identification of regions/stations with higher predictability, which has important implications for interpolation or extrapolation of streamflow data.
流域分类在许多与水资源和环境相关的应用中发挥着重要作用。近年来，一些研究将非线性动力学和混沌概念应用于流域分类，主要使用维度度量。本研究通过应用非线性局部近似预测方法，探索将预测作为流域分类的一种度量。该方法使用时间序列的相空间重构概念来表示潜在的系统动力学，并在相空间中识别系统演化和预测的最近邻点。预测精度度量以及该方法中涉及的参数（例如相空间或嵌入维数、邻点数）的最优值用于分类。为了实施，该方法应用于澳大利亚218个流域的日流量数据，并针对不同的嵌入维数和邻点数进行预测。预测结果表明，仅使用流量进行相空间重构就能提供良好的预测。结果还表明，对于较低的嵌入维数和较少的邻点数能实现更好的预测，这表明流量动力学可能具有低维性。基于预测精度的分类结果被发现对于识别具有较高可预测性的区域/站点很有用，这对流量数据的插值或外推具有重要意义。
