Daily Streamflow Forecasting Based on Flow Pattern Recognition

Accurate streamflow prediction is of great significance for water resource management. In recent years, data-driven models such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely used in the field of flow prediction. However, traditional data-driven models neglect the extraction and utilization of the data's own characteristics. This study proposes a daily flow prediction model based on the pattern recognition of flow sequences. Based on the input number of the prediction model derived from the partial autocorrelation function, the flow sequence was divided into subsequences. Five patterns of flow subsequences, including monotonic rising, monotonic falling, monotonic stable, concave, and convex, were then identified, which helped to explore the characteristics of the flow subsequences. For each pattern, traditional ANN and SVM models were applied to predict the flow. A comparison with the traditional ANN and SVM models shows that the hybrid models of the pattern recognition method (PRM) and the traditional ANN and SVM have higher accuracy. The Nash efficiency coefficient (NSE) of the hybrid PRM-SVM model was as high as 0.9815, and the mean absolute percentage error (MAPE) was only 6.75%. In addition, the prediction accuracy of the flood peak also improved. The average relative error of the peak flood derived from the hybrid PRM-ANN and PRM-SVM models were reduced by 0.12% and 0.40%, respectively, compared with the traditional ANN and SVM models.

Automated summary

Accurate streamflow prediction is crucial for water resource management, and data-driven models like artificial neural networks and support vector machines have been widely used. However, traditional models often overlook the unique characteristics of the data. This study introduces a daily flow prediction model based on pattern recognition of flow sequences, which outperforms traditional ANN and SVM models in accuracy, especially in predicting flood peaks.