A Novel Intelligent Model for Monthly Streamflow Prediction Using Similarity-Derived Method

Abstract

Accurate monthly streamflow prediction is crucial for effective flood mitigation and water resource management. The present study proposes an innovative similarity-derived model (SDM), developed based on the observation that similar monthly streamflow patterns recur across different years under comparable hydrological and climate conditions. The model is applied to the Lancang River Basin in China. The model performance is compared with the commonly used support vector machine (SVM) and Mean methods. Evaluation measures such as RMSE, MAPE, and NSE confirm that SDM6 with a reference period of six months achieves the best performance, improving the Mean model by 79.9 m3/s in RMSE, 6.07% in MAPE, and 8.62% in NSE, and the SVM by 53.65 m3/s, 0.24%, and 5.53%, respectively.