1) monthly discharge prediction
月径流预测
2) monthly runoff forecast
月径流预报
1.
According to the character of runoff changing,a wavelet-ANFIS model is presented for reservoir monthly runoff forecast.
该模型用于淮河支流沙河上游年月径流变化幅度较大的昭平台水库月径流预报中,结果表明所建模型能够较好地预报原始信号的趋势,预报精度比单一ANFIS预报模型有较大改善,但仍有待提高。
3) Runoff Prediction
径流预测
1.
Daily runoff prediction based on integration of genetic and back-propagation algorithms;
基于遗传BP算法的日径流预测
2.
The study on runoff prediction model of BP neural network based on phase space and GA;
基于相空间遗传BP神经网络的径流预测研究
3.
Study of runoff prediction based on fractal interpolation theory;
基于分形插值理论的径流预测探讨
4) runoff predicting
径流预测
1.
The runoff predicting results are obtained from two different kinds of BP neural networks,it is shown that convergence speed and precision of BP neural networks are improved.
最后利用参数优化前后的BP模型进行径流预测,结果表明模型收敛速度和精度明显提高。
5) runoff forecast
径流预测
1.
Application of fuzzy support vector machine to runoff forecast;
模糊支持向量机在径流预测中的应用
2.
Wavelet neural networks model used for runoff forecast based on fuzzy C-means clustering
基于FCM的小波神经网络模型在径流预测中的应用
3.
The optimal network is successfully applied in runoff forecast, it is more efficient and precise.
在水库径流预测中应用模糊聚类分析方法优选网络结构 ,优化后的网络具有较高的效率 ,并取得了较好的拟合、预测精
6) runoff forecasting
径流预测
1.
This paper presents a daily runoff forecasting method by using wavelet neural networks,the fuzzy C-means clustering analysis and the wavelet neural network.
提出了一种基于GIS与小波神经网络方法相结合构建而成的水库日径流预测模型(GWNNR),通过模糊C均值聚类分析将水库历史径流数据分成4类,并分别建立相应的小波神经网络预测模型,应用遗传算法(Genetic algorithm)和误差反传递(Back-propagation)算法对模型的参数进行优化,对某水库2005年日平均来流进行分类预测,结果表明,该方法具有较好的训练速度和较高的预测精度。
2.
This paper analyses the relations of forecasting result using wavelet periodic analysis model with the length of runoff series、time interval as well as the drainage basin scale from a typical reservoir inflow runoff forecasting example.
通过典型水库入库径流预测实例,分析了小波周期分析模型预测效果与径流系列长度、时段长及流域尺度之间的关系。
补充资料：发育进度预测法(见发生期预测)
发育进度预测法(见发生期预测)
发育进度预测法见发生期预测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条