1) 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。
2) annual runoff forecast
年径流预测
1.
The problem of annual runoff forecast considering the influence of the uncertainty of original data was studied by using the rough set theory in this paper.
年径流预测对合理开发和优化利用水资源、更好地制定区域社会经济规划具有十分重要的指导意义。
2.
According to the characteristics of the long-time runoff forecasting,a new annual runoff forecasting model based-on the combination of genetic algorithm and neural network is proposed in this paper.
根据径流中长期预测的特点,将遗传算法和神经网络相结合,提出一种年径流预测新算法———遗传神经网络优化预测方法。
3) annual runoff prediction
年径流预测
1.
To increase the efficiency of medium and annual runoff prediction,the grey self-memory model based on GM(1,1) differential equation was established.
为探索提高年径流预测精度的简单有效途径,以GM(1,1)微分方程为动力核建立了灰色自记忆模型,并应用于寸滩站年径流的拟合和预测中,取得了良好的效果。
2.
Based on the primary qualitative method,we propose an annual runoff prediction model using weighted sum of wavelet coefficients of major periods to predict the periodic components.
本文在原有定性分析方法的基础上,提出基于年径流时间序列主周期小波系数加权求和预测周期成分的年径流预测模型。
4) 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。
5) logistics quantity of year
年物流量预测
6) daily runoff forecasting
日径流量预测
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