Exploring geometrical patterns in streamflow time series: Utility for forecasting?

Abstract

Streamflow time series often provide valuable insights into the underlying physical processes that govern responses of any watershed to storm events. Patterns derived from time series based on repeated structures within these series can be beneficial for developing new or improved data-driven forecasting models. Data-driven models, artificial neural networks (ANN), are developed in the current study for streamflow prediction using input structures that are classified by geometrically similar patterns. A new modular and integrated ANN architecture that combines multiple ANN models, referred to as pattern-classified neural network (PCNN), is proposed, developed and investigated in this study. The PCNN relies on the development of several independent local models instead of one global data-driven prediction model. The PCNN models are evaluated for one step-ahead prediction of daily streamflows for Reed Creek and Little River, Virginia, and Elkhorn Creek, Kentucky in the United States. Results obtained from this study suggest that the use of these patterns has improved the performance of the neural networks in prediction. The improved performance of the PCNN models can be attributed to prior classification of data benefiting generalization abilities. PCNN model outputs can also provide an ensemble of forecasts that help quantify forecast uncertainty.