A Novel Intelligent Model for Monthly Streamflow Prediction Using Similarity-Derived Method
Water(2023)
摘要
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.
更多查看译文
关键词
monthly streamflow prediction,similarity-derived model (SDM),Lancang River basin,hydrological processes,performance evaluation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要