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复杂河道洪水预报系统研究——以淮河王家坝至小柳巷区间流域为例 总被引：2，自引：1，他引：1
随着社会经济的快速发展,洪水灾害造成的损失日益严重.洪水预报作为一项重要的防洪非工程措施,对防洪、抗洪工作起着至关重要的作用.淮河洪水危害的严重性和洪水演进过程的复杂性使得淮河洪水预报系统的研究长期以来受到高度重视.本文以王家坝至小柳巷区间流域为例,以河道洪水演算为主线,采用新安江三水源模型进行子流域降雨径流预报,概化具有行蓄洪区的干流河道,进行支流与干流、行蓄洪区与干流的洪水汇流耦合计算,采用实时更新的基于多元回归的方法确定水位流量关系,并以上游站点降雨径流预报模型提供的流量作为上边界条件、以下游站点的水位流量关系作为下边界条件,结合行蓄洪调度模型,建立具有行蓄洪区的河道洪水预报系统,再与基于K-最近邻(KNN)的非参数实时校正模型耦合,建立淮河中游河道洪水预报系统.采用多年资料模拟取得了较好的预报效果,并以2003和2007年大洪水为例进行检验,模拟结果精度较高,也证明了所建预报系统的合理性和适用性. 相似文献
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通过对太湖流域平望水位和米市渡潮位过程及其影响因子的研究，提出了潮位过程的平均潮位、潮差、潮位过程平移、潮位过程分解与重建等概念，并用简单实用的统计相关方法建立了平望水位和米市渡潮位过程预报模型；用1996—1999年汛期(5月1日—9月30日)的水文观测资料对所建立的模型进行了率定，率定结果表明，所建立的模型具有一定的预报精度、对太湖流域洪水预报调度具有重要的参考作用。 相似文献
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辨析洞庭湖历史水位演变态势对保护湖泊生态系统健康运转以及确保长江中下游防洪安全和水资源利用均至关重要。为了定量评估洞庭湖近60年来水位变化特征、程度及规律,基于洞庭湖鹿角、杨柳潭和南咀水文站1961—2020年逐日水位监测数据,应用Mann-Kendall检验法、小波分析法、累积距平法、滑动t检验法等方法对洞庭湖水位变化趋势性、周期性、突变性进行分析,进而采用IHA-RVA法综合评价洞庭湖突变年前后各站点水位改变度和整体水位改变度。研究结果表明:(1) 1961—2020年,鹿角站和杨柳潭站年均水位呈上升趋势,南咀站年均水位呈下降趋势;3个站点水位周期性变化较为明显,呈现4～5个时间尺度的周期变化规律,第一主周期为55～56年;鹿角站水位突变年份为1979、2003年,杨柳潭站水位突变年份为1978、2003年,南咀站水位突变年份为2003年,综合确定2003年为3个站点突变年份。(2)通过分析突变前后3个站点的水位、时间、频率、延时和改变率5组32个水位指标改变度,发现杨柳潭站水位改变度大于鹿角站和南咀站,鹿角、杨柳潭、南咀站的整体水位改变度分别为43%、48%、42%,均属于中度改... 相似文献
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在Ｋａｇａｎ的计算中，删去了许多成功的ＶＡＮ预报。而且，Ｋａｇａｎ假定了一个任意的地震截止震级。我们认为，对ＳＩ－ＮＯＡ目录，ＶＡＮ的高成功率既不是由顺去调整预报规则造成的，也不是地震活动性的非随机性引起的。当考虑自１９８６年以来由ＶＡＮ提出的预报规则时，如何我们从目录中去掉相关事件预报效果在统计上仍然是显著的，并且与主震有关的许多成功预报是不允许的。 相似文献
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目前地震预报还没有过关,单靠某一个前兆观测台站和某一种前兆手段来预报地震都不是很准确的。因此,利用多个台站、多种前兆观测手段来预报地震就成为提高预报准确率的一种重要途径。但是,在实际预报过程中,有一个概率问题需要解决,即当所有前兆观测项目中(每一个前兆观测台站的每一种手段都可称为一个前兆观测项目),有一些出现异常, 相似文献
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太湖水面蒸发量预报模型及其应用 总被引：1，自引：2，他引：1
介绍了几种太湖水面蒸发量的数学模型和预报模型,并用其预测伏旱和夏涝期间旬、月的湖面蒸发量。最后提出应用湖面蒸发量进行太湖水位预报的方法。 相似文献
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为了解四川德阳地下水位动态，进而分析地震前兆动态，本文设计了一个基于BP神经网络的地下水位预测系统。采用SWY-Ⅱ数字式水位仪对德阳地下水位数据进行采集。根据采集的2015年水位数据，利用BP神经网络对地下水位变化进行预测，以一年的采集数据进行训练和测试，采用3个输入节点、1个输出节点设计了BP神经网络结构。为了进一步验证本预测系统，本文对2017年7月1日—10月26日地下水位情况进行了预测。实验表明:该方案能有效实现地下水位的预测，为地震前兆工作提供可靠数据。 相似文献
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Xiupeng Wei Andrew Kusiak 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(1):241-249
Predicting influent flow is important in the management of a wastewater treatment plant (WWTP). Because influent flow includes municipal sewage and rainfall runoff, it exhibits nonlinear spatial and temporal behavior and therefore makes it difficult to model. In this paper, a neural network approach is used to predict influent flow in the WWTP. The model inputs include historical influent data collected at a local WWTP, rainfall data and radar reflectivity data collected by the local weather station. A static multi-layer perceptron neural network performs well for the current time prediction but a time lag occurs and increases with the time horizon. A dynamic neural network with an online corrector is proposed to solve the time lag problem and increase the prediction accuracy for longer time horizons. The computational results show that the proposed neural network accurately predicts the influent flow for time horizons up to 300 min. 相似文献
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A model of rainfall–runoff relationships is an essential tool in the process of evaluation of water resources projects. In this paper, we applied an artificial neural network (ANN) based model for flow prediction using the data for a catchment in a semi‐arid region in Morocco. Use of this method for non‐linear modelling has been demonstrated in several scientific fields such as biology, geology, chemistry and physics. The performance of the developed neural network‐based model was compared against multiple linear regression‐based model using the same observed data. It was found that the neural network model consistently gives superior predictions. Based on the results of this study, artificial neural network modelling appears to be a promising technique for the prediction of flow for catchments in semi‐arid regions. Accordingly, the neural network method can be applied to various hydrological systems where other models may be inappropriate. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
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This paper presents the development of a multiple‐station neural network for predicting tidal currents across a coastal inlet. Unlike traditional hydrodynamic models, the neural network model does not need inputs of coastal topography and bathymetry, grids, surface and bottom frictions, and turbulent eddy viscosity. Without solving hydrodynamic equations, the neural network model applies an interconnected neural network to correlate the inputs of boundary forcing of water levels at a remote station to the outputs of tidal currents at multiple stations across a local coastal inlet. Coefficients in the neural network model are trained using a continuous dataset consisting of inputs of water levels at a remote station and outputs of tidal currents at the inlet, and verified using another independent input and output dataset. Once the neural network model has been satisfactorily trained and verified, it can be used to predict tidal currents at a coastal inlet from the inputs of water levels at a remote station. For the case study at Shinnecock Inlet in the southern shore of New York, tidal currents at nine stations across the inlet were predicted by the neural network model using water level data located from a station about 70 km away from the inlet. A continuous dataset in May 2000 was used for the training, and another dataset in July 2000 was used for the verification of the neural network model. Comparing model predictions and observations indicates correlation coefficients range from 0·95 to 0·98, and the root‐mean‐square error ranges from 0·04 to 0·08 m s?1 at the nine current locations across the inlet. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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李强 《地震学报(英文版)》2000,13(4):434-439
IntroductionThere are some problems we often meet when we work for earthquake forecasting with theobservational data of earthquake precursor observation. Such items as the deformation of earth'scrust, underground fluid, geoelectricity and so on. These problems include that the ceasing workof the observational apparatus because of malfunction or accident in case of emergent ewthquakesituation will lose some imperative information and make it more difficult to evaluate futUreearthquake situation… 相似文献
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人工神经网络是用来模拟人脑智能特点和结构的一种模型,具有很强的非线性映射功能．把它引用到地震前兆观测数据的分析处理中,可为前兆观测更好地服务于地震分析预报开辟出一条新路,也是对人工神经网络方法应用的推广．本文分析了时间序列的可预测性,给出了用人工神经网络预测地震前兆混沌时间序列的方法,并以江宁台和徐州台SQ 型地倾斜仪观测及溧阳台体应变观测的时间序列为例,对其作了预测和处理．结果表明:用该方法处理达到的精度能满足实际工作的需要,因而该方法在今后的实际地震分析预报工作中具有重要应用价值． 相似文献
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Two models, one linear and one non‐linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non‐linear model is based on a multilayer feed‐forward back propagation (FFBP) artificial neural network (ANN) and uses flow‐stage data measured at the upstream and downstream stations. ANN predicted the real‐time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow‐stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4‐h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8‐h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level 总被引：3，自引：1，他引：2
Cheng Lian Zhigang Zeng Wei Yao Huiming Tang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(8):1957-1972
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China. 相似文献
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IINTRODUCTIONIntherecentdecadesfrequentflooddisasterscausedseriousdamagesandclaimedthousandsoflives,suchasthe1998floodintheYangtzeRiverandthe1996floodintheYellowRiver.The1998floodintheYangtzeandtheSonghuaRiversbroughtdirectlossesofmorethan$30billions.Lowdischargehighstageisthemaincharacterofthefloods.Forexample,thehighestfloodstagein1998wasI.sinhigheranddischargewas14000m3/slowerthanthosein1954atLuoshanStationoftheVangtzeRiver.Anewmodelisrequiredtobedevelopedforaccuratepredictionoffl… 相似文献
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Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting. 相似文献