新安江模型和改进BP神经网络模型在闽江水文预报中的应用

精确的水文预报是防洪减灾中重要的非工程措施,水文模型是开展水文预报最有力的工具。采用LM算法改进了的BP神经网络水文预报模型,以闽江富屯溪流域为例,进行了BP模型和新安江模型在日流量模拟预报中的应用比较。结果表明:两个模型总体均达到水文预报的精度要求,水文预报合格率可达到90%以上;新安江模型在丰水年模拟效果较好,相比而言,BP神经网络模型的模拟精度更高一些;两个模型均可用于闽江流域的水文预报研究。

Application of Xin'anjiang model and the improved BP neural network model in hydrological forecasting of the Min River

Accurate hydrological forecasting is an important non-engineering measure in flood disaster relief. Hydrologic models are the most useful tool for hydrological forecasting. The BP neural network model was improved by introducing LM algorithm, together with the Xin''anjiang Model, their applications for daily flow simulating and forecasting to the Futun River of the Min River were compared. The results showed that, both hydrologic models reached the accuracy requirements of hydrological forecasting with over 90% of hydrological forcasting qualified rate. The Xin''anjiang model performed better for the wet years while the improved BP model was better in simulating accuracy than the Xin''anjiang model. Both models were applicable to the hydrological forecasting of Min River.