简要介绍了人工神经网络用于洪水预报的基本原理，对降雨径流预报的网络模型进行了改进。
This article presents the principle of ANN briefly in the application of flood forecast and an improved network algorithm of rainfall runoff forecasting.
中期模型优化的时间跨度等于中期入库径流预报的预见期（3-7天），优化时段为一天。
The time span of middle-term optimization model equal foreseeable period of runoff forecasting (3 to 7 days), and the optimization period is one day.
这说明运用马尔可夫模型进行河径流量的丰枯状态预报是有效可行的。
So, it is practical to use the sequential clustering and Markov model to forecast the river runoff .
将偏最小二乘回归与神经网络耦合，建立了径流量预报模型。
Coupling partial least-squares regression and neural network in the article, the forecasting model of the quantity of runoff is established.
建立GM（1，1）灰色拓扑模型群，通过结论分析，表明该模型在年径流预报中为一种较为理想的方法。
Setting up GM(1,1) grey topological model groups and through analyzing conclusion, it indicates that such model is a kind of comparatively ideal method in predicting annual surface flow.
建立GM（1，1）灰色拓扑模型群，通过结论分析，表明该模型在年径流预报中为一种较为理想的方法。
Setting up GM(1,1) grey topological model groups and through analyzing conclusion, it indicates that such model is a kind of comparatively ideal method in predicting annual surface flow.
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