共查询到18条相似文献,搜索用时 281 毫秒
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河道洪水实时预报模型及在寸滩—螺山河段的应用 总被引:2,自引:0,他引:2
采用水力学和水文学相结合的方法,根据河道非恒定流的圣维南方程组,建立了河道洪水相应涨至预报模型和扩散波洪水预报模型,既可预报流量,又可预报水位。在模型的实时校正技术中,采用了具有时变遗忘因子的递推最小二乘实时校正算法,提高了参数实时跟踪能力和辨识精度。相应涨差实时预报模型和扩散波实时预报模型在寸滩-螺山河段上的应用均获得较好的效果。 相似文献
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大流域洪水预报与洪水调度管理方法研究 总被引:9,自引:3,他引:6
采用水文学与水力学、确定与随机相结合的方法,研究大流域洪水预报与洪水调度管理。确定性模型采用降雨~径流模型的新安江模型和经验预报方案进行流域流量过程预报,一维非恒定流水力学方法进行河道流量与水位以及蓄滞洪预报。随机模型采用线性动态模型进行下断面水位实时预报,最小二乘递推模型进行误差实时校正。以长江支流修水流域为例,采用新安江模型、经验方案进行万家埠与柘林水库入库洪水预报作为水力学计算的上边界条件,采用一维非恒定流水力学方法进行河道洪水流量、水位计算以及分洪计算。该方法为流域的防洪减灾提供了科学的途径。 相似文献
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改进最小二乘递推算法的洪水预报应用研究 总被引:2,自引:0,他引:2
建立线性自回归模型,应用于洪水实时预报,并应用AIC、BIC这两种准则以确定自回归模型的阶数。最小二乘递推算法是估计自回归参数的一种常见方法。最小二乘法估算出的模型参数在预报误差平方和最小的条件下是最优解。研究中,为了强化时变系统的辩识以提高洪水预报精度,对数据采取衰减记忆、有限记忆及时变衰减记忆的方式,对基本的最小二乘递推算法提出了三种改进形式,并利用这几种改进算法进行了洪水演算,最后对几种算法的演算结果进行了比较。 相似文献
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洪水预报实时校正技术研究综述 总被引:3,自引:0,他引:3
对典型的水文模型流量预报实时校正算法、误差自回归校正算法、衰减记忆最小二乘算法和卡尔曼滤波算法等洪水预报实时校正方法及其求解过程进行了分析,讨论了各种方法的利弊.结果表明:水文模型流量预报实时校正算法物理意义明确,容易编制程序,但预见期较长时的校正效果不明显,对于有多个水文站的较大流域,不适宜采用该法;误差自回归校正算法的阶数可以通过实际情况来确定,模型可以确定系数,也可以加入不断反馈的信息而成为变系数的时变模型;衰减记忆最小二乘算法仅适用于缓时变系统的参数识别,对于非确定性动态水文系统模型参数跟踪乏力,容易形成参数在线跟踪的滞后性;卡尔曼滤波技术目前应用较为广泛,但在实际使用中仍然存在各种各样的问题,需要借助滤波处理技术加以改善. 相似文献
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水动力学模型实时校正方法研究 总被引:7,自引:1,他引:6
多年来,国内外学者对水动力学模型应用于洪水预报做了大量研究,然而,由于预报精度不理想,长期以来未能在实时作业预报中得以广泛应用.为提高水动力学模型在洪水实时作业预报中的计算精度,研究从一维水动力学模型差分方程出发,通过对糙率参数的影响试验,研发出一种利用新息变化自动校正糙率系数的算法.该算法通过实时校正糙率系数达到对一维水动力学模型预报误差的实时校正.在长江干流清溪场至万县河段试验结果表明:目标函数一步及多步预报误差均有大幅降低,取得了提高预报精度的显著效果.本算法不改变水动力学模型的递推和使用糙率系数的算法结构,可以直接作为附加校正器加入到原水动力学计算程序中去,应用方便. 相似文献
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文章介绍了清河水库在洪水预报中,应用蓄满产流模型、大伙房模型、新安江模型进行洪水预报。在实际预报过程中利用经验和实时修正等方法,对各个模型的预报结果进行分析,应用最小二乘法等科学的计算方式综合各个预报结果,发布预报数值。 相似文献
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多元线性因归方程被广泛地应用于河道洪水演算。考虑上、下游,干、支流来水的影响,建立了多元线性回归模型。最新时刻的预报误差即新息依时序组成误差序列,带时变遗忘因子在线递实时预报方法,对该误差序列用衰减记忆最小二乘法在线识别,从当前现实的预报误差外推未来的预报误差,以实时校正预报结果。将该模型试用于红水河三个子流域后发现,模型具有物理概念明确,适用性强,便于移植等特点,万为突出的表现为,不但在洪水传播时间之内即传统意义上的预见期内的预报精度高,达到了规范要求的甲等方案标准,而且能对传播时间之外的未来洪水作出趋势预报,这对施工洪水预报用至水库洪水调度都是十分有益的。 相似文献
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REAL-TIME FLOOD FORECASTING METHOD WITH 1-D UNSTEADY FLOW MODEL 总被引:4,自引:0,他引:4
MU Jin-bin ZHANG Xiao-feng 《水动力学研究与进展(B辑)》2007,19(2):150-154
A real-time forecasting method coupled with the 1-D unsteady flow model with the recursive least-square method was developed. The 1-D unsteady flow model was modified by using the time-variant parameter and revising it dynamically through introducing a variable weighted forgetting factor,such that the output of the model could be adjusted for the real time forecasting of floods. The application of the new real time forecasting model in the reach from Yichang to Luoshan of the Yangtze River was demonstrated. Computational result shows that the forecasting accuracy of the new model is much higher than that of the original 1-D unsteady flow model. The method developed is effective for flood forecasting,and can be used for practical operation in the flood forecasting. 相似文献
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Han D. Cluckie I. D. Karbassioun D. Lawry J. Krauskopf B. 《Water Resources Management》2002,16(6):431-445
A modern real time flood forecasting system requires itsmathematical model(s) to handle highly complex rainfall runoffprocesses. Uncertainty in real time flood forecasting willinvolve a variety of components such as measurement noise fromtelemetry systems, inadequacy of the models, insufficiency ofcatchment conditions, etc. Probabilistic forecasting is becomingmore and more important in this field. This article describes a novel attempt to use a Fuzzy Logic approach for river flow modelling based on fuzzy decision trees. These trees are learntfrom data using the MA-ID3 algorithm. This is an extension of Quinlan's ID3 and is based on mass assignments. MA-ID3 allows for the incorporation of fuzzy attribute and class values intodecision trees aiding generalisation and providing a framework for representing linguistic rules. The article showed that with only five fuzzy labels, the FDT model performed reasonably welland a comparison with a Neural Network model (Back Propagation)was carried out. Furthermore, the FDT model indicated that therainfall values of four or five days before the prediction time are regarded as more informative to the prediction than the morerecent ones. Although its performance is not as good as the neural network model in the test case, its glass box nature couldprovide some useful insight about the hydrological processes. 相似文献
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针对西江干流河道特点,考虑洪水归槽的影响,建立了河道一维非恒定流洪水演进数学模型,采用Preissmann四点偏心隐式差分格式离散圣维南方程组,应用追赶法求解非恒定流圣维南方程组。根据实测资料对西江干流(大藤峡至高要段)洪水演进过程进行验证,结果表明:模型能较好的模拟流量变化过程,模拟值与实测值符合较好,验证了该模型是合理可行的。可用于洪水预报、枯季调水等研究。 相似文献
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Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting 总被引:3,自引:3,他引:0
Reconstruction and/or modification of an already existing fuzzy model with new data may improve system performances. As new
data become available, adjusting the existing fuzzy rule-based model may present a challenging alternative to full model reconstruction.
In this paper a fuzzy rule-based control model using a Takagi–Sugeno fuzzy system is presented and a model modification algorithm
is developed which improves the performance of the initial model as new data become available. Proposed approach is applied
to a flood flow forecasting case example and the results are compared with those forecasted using initially available and
reconstructed models. Results show that the modified model outperforms the initial FRB model. Reconstructed model performs
slightly better than the modified model; however, the reconstruction may not be justified in a real time flood forecasting
system, considering the limitations on the available lead time. 相似文献
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《水动力学研究与进展(B辑)》2018,(5)
An accurate and reliable real-time flood forecast is crucial for mitigating flood disasters. The errors associated with the inflow boundary forcing data are considered as an important source of uncertainties in hydraulic model. In this paper, a real-time probabilistic channel flood forecasting model is developed with a novel function to incorporate the uncertainty of the forcing inflow. This new approach couples a hydraulic model with the particle filter(PF) data assimilation algorithm, a sequential Bayesian Monte Carlo method. The stage observations at hydrological stations are assimilated at each time step to update the model states in order to improve the next time step's forecasting. This new approach is tested against a real flood event occurred in the upper Yangtze River. As compared with the open loop simulations, the evaluations of model performance with several deterministic and probabilistic metrics indicate that the accuracy of the ensemble mean prediction and the reliability of the uncertainty quantification are improved pronouncedly as a result of the PF assimilation. Further assessment of the prediction results at different lead times shows that the improvement of model performance deteriorates with the increase of the lead time due to the gradual diminishing of the updating effect for the initial conditions. Based on the analyses of the number of particles and the assimilation frequency, we find that the optimal number of particles can be determined by balancing the model performance and the computation cost, while a high assimilation frequency is preferred to incorporate the emerging observations to update the model states to match the real conditions. 相似文献
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Abstract In non-structural measurement of flood control, hydrologic forecasting plays a very important role. Owing to time-variance, non-linearity, and uncertainty of hydrological processes, realtime forecasting has become an efficient approach. The paper addresses an important practical problem: improving short-term hydrological forecasting based on real-time updating in the operation model. A simple nonlinear model with a variable gain parameter (VGPM) is developed. A separated calibration approach for updating parameters used in the runoff generation process and the response function in the flow routing is proposed. State space equations associated with updating model parameters in a real time scheme were developed. The VGPM approach is verified for three types of representative watersheds. The performances of different updating schemes in rainfall-runoff modeling and real-time forecasting were tested. The results indicate that significant improvement in the efficiency of hydrological modeling can be obtained from the VGPM approach, relative to simple linear models (SLM). For the watersheds with a time-variant characteristic, moreover, significant improvement in the hydrologic forecasting efficiency can be obtained by adaptive schemes. The efficiency of real-time modeling by the self-adaptive Kalman Filtering algorithm was found to be very close to that of the Recursive Least-Square method. 相似文献