水文时间序列相似性查找模型。
本文提出了一种混沌水文时间序列区间预测算法。
An interval prediction algorithm for chaotic hydrological time series is proposed.
在水文及水资源领域，很多问题的研究对象都涉及到水文时间序列。
The study object of many problems comes down to hydrologic time series in the field of hydrology and water resources.
混沌和支持向量机理论为研究复杂多变的非线性水文时间序列开辟了新的途径。
Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series.
第二部分基于水文序列变化的混沌特性，对水文时间序列的混沌预测模型进行了研究。
In the second part, the chaotic prediction models for hydrological time series are studied in terms of the chaotic characteristics of hydrological evolution process.
水文时间序列相似性查询可用于雨洪过程预测、环境演变分析、水文过程规律分析等方面。
Hydrological time series similarity search can be used for rainfall and flood forecasting, the analysis of environment evolvement and hydrological process, etc.
该方法通过频谱分析，可以计算出全局性的主要周期，从而反映出水文时间序列的整体特性。
By spectral analyzing, the overall period can be calculated, thus the holistic characteristics of hydrological time series can be reflected.
本文从理论基础、实测样本计算和统计试验方法三个方面对水文时间序列的周期分析检测方法进行了分析研究。
This thesis discusses period analysis methods of hydrological time series, and compares them from theory, measured sample calculation and Monte-Ca.
由于干旱地区气候干燥、降水稀少、蒸发强烈，使得水文过程呈现出非常复杂的变化过程，水文时间序列表现出高度的非线性和不确定性。
In this region, dry climate, rare rain and strong evaporation make hydrological process show very complex change process, hydrological time series present highly nonlinear and uncertainty.
论文在深入研究和比较各种方法的基础上，探索适合水文数据特点的时间序列相似性搜索的方法。
Based on the research and comparison of different methods, this paper explored the similarity search method of time series which is adaptive to the characteristics of hydrological data.
周期均值叠加法将随时间变化的水文要素序列分离成若干个周期波，然后将周期波进行外延，再进行线性叠加，从而获得预报结果。
The periodic mean superposition method can predict the result through decomposing the hydrological time series into several periodic waves, extrapolating the periodic waves, and linear superposition.
时间序列分析法在水文规律分析、水文模拟以及水文预报等许多方面都起着重要作用。
Time series analysis method is playing an important role in hydrologic regular analysis and hydrologic analogy as well as hydrologic forecasting and so forth.
水文水资源序列是一个具有周期变化、随机变化和递增或递减趋势变化的复杂时间序列。
Hydrologic sequence is a complicated time sequence, which has characteristics such as periodic changes, random changes and increasing or decreasing trend changes.
研究结果表明，小波变换是分析非平稳随机时间序列的有效工具，在水文水资源领域应用潜力很大。
The results indicate that the wavelet transform is an effective tool for nonstationary stochastic series analysis, which has great potential in hydrology and water resources research.
在水文预测中，应用混沌分析方法需解决相空间重构、时间序列的混沌性识别和混沌时间序列预测等关键技术。
In hydrological prediction, it is necessary that t he reconstruction of chaotics Tate space and the diagnosis of chaotic behavior as well as the chaotic pre diction are solved.
用这两种方法对黄河潼关水文站水沙序列进行了预测，经计算表明，改进的时间序列方法有效地提高了预测精度。
These two methods were used at Tongguan hydrometric station to test flow and sediment forecasting and the results computed have shown efficiently improved accuracy of prediction.
研究还表明，该方法在水文月径流时间序列的预测中同样有效。
On runoff time series forecasting, this method shows a good result, too. 3.
通过长系列的降雨资料确定了水文年，通过考虑时间序列预测了未来一段时期的水文状况。
The hydrological year was determined by long series rainfall data and the hydrological regime was predicated by considering time series method.
通过长系列的降雨资料确定了水文年，通过考虑时间序列预测了未来一段时期的水文状况。
The hydrological year was determined by long series rainfall data and the hydrological regime was predicated by considering time series method.
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