1) runoff forecasting
径流量预测
1.
Based on the runoff forecasting of some gauge stations in Tarim River Basin participated,the validity and some problems of GM(1,1) and BP algorithm existed in runoff forecasting were discussed.
因径流量预测中伴随着随机性、灰信息性的特点,传统的数学模型难以解决复杂的非结构化问题。
2.
A modified BP model is applied to dailyrunoff forecasting for Huai river, and good precision is obtained.
并以淮河流域为例,将经过改进的BP网络模型应用于流域日径流量预测中,得到了较高的模拟精度。
2) annual runoff prediction
年径流量预测
1.
Application of the time series decomposable model for the annual runoff prediction in Wulasitai River;
时间序列分解模型在乌拉斯台河年径流量预测中的应用研究
2.
This paper takes annual runoff prediction and an optimal allocation of water as research aims,the work and technical implementation including the following three aspects:First,on the basis of the research on the irrigation area in annual runoff and influencing factors,analyzes the characteristics of the annual runoff,and compara the time rank,artificial neural network and support vector machine.
本文以年径流量预测与灌区优化配水为研究目标,主要进行的工作及技术实现包括以下三个方面:首先,在对灌区年径流量相关概念与影响因素研究的基础上,分析了年径流量具有的特点,同时比较分析了年径流量的预测方法,针对年径流量具有的特点,选取适宜的预测方法,确定采用支持向量机方法进行年径流量预测;其次,年径流量的变化情势受气候、下垫面、太阳活动与人类活动等因素的影响,这些影响因素的复杂性与不确定性,给年径流量预测带来了极大的难度,基于支持向量机方法能够较好地解决小样本、高维、非线性问题,建立年径流量SVM预测模型,所建立的模型预测结果精度高,证明了该方法应用的合理性和有效性;最后,针对灌区水资源不足时的非充分灌溉情况,为了使灌区取得最大的灌溉经济效益,建立灌区优化配水模型,利用遗传算法对所建立的优化模型求解,结果显示采用遗传算法可以获得较好的寻优效果,有效提高了灌区灌溉经济效益。
3) daily runoff forecasting
日径流量预测
4) 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
5) 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。
6) 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
补充资料：经验指数预测法(见发生量预测)
经验指数预测法(见发生量预测)
经验指数预测法见发生量预测。
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