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根据灌溉用水决策支持系统(DSS)和实时配水的要求,建立了霍泉泉源年出流量随机预测模型和月出流量实时预测模型.经分析表明,随机AR(p)预测模型较适合预测泉源的年出流量,月出流量实时预测模型的预测精度也较高 相似文献
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利用相空间重构技术,并借助G—P算法、C-C方法和Wolf方法从宁陵地区地下水位一维时间序列中提取Lyapunov指数,结果表明此时间序列具有混沌特征。计算了宁陵地区地下水位时间序列的关联维数、时间延迟和最大Lyapunov指数,将局域加权一阶多步预测模型应用于地下水位预测。预测表明,此模型可有效应用于地下水位时间序列的多步预测。 相似文献
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济南岩溶含水系统的泉水位和泉流量明显滞迟于降水量。利用峰值时段差、原始序列回归等方法,并考虑了降水量、泉水位、泉流量周期性的影响,计算出济南岩溶含水系统的滞迟时间为2~6个月。以不同滞迟时间建立了泉流量预测模型,并进行了对比分析,滞迟时间6个月预测模型,预测精度较高,物理概念不明确;滞迟时间2个月预测模型,预测精度稍差,但物理概念明确,建议采用该模型进行泉流量预测。 相似文献
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运用灰色系统理论建立GM(1,1)预测模型,以1990-2007年民勤盆地地下水位下降最为严重的下游湖区站点资料,预测了2007-2012年湖区地下水水位动态。结果表明:预测数据和实测数据达到了较好的拟合,其中逐年相对误差最大的年份(1993年)不超过15%,精度为90%,后验比为0.246。若不采取有效措施而任其发展,至2012年该湖区地下水位埋深将达到50 m,严重威胁到下游人畜的饮水问题。 相似文献
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为解决以往模型未考虑地下水位相关影响因素的问题,探讨长短期记忆(LSTM)神经网络在地下水位预测中的应用,利用长短期记忆神经网络,采用多变量输入的方式,构建了基于多变量LSTM神经网络的地下水水位预测模型。以泰安市岱岳区J1号监测井为例,采用2001-2014年地下水水位动态监测资料与相关影响因素数据,利用多变量LSTM神经网络对2015-2016年地下水位进行预测,并与单变量LSTM神经网络和反向传播(BP)神经网络进行对比。研究结果表明:以相关影响变量为输入的BP神经网络无法考虑时序变化规律,预测均方根误差最大,为2.399 3;以地下水位为变量输入的单变量LSTM神经网络仅能根据时序变化作出相应预测,无法考虑相关变量影响,预测均方根误差为2.102 2;基于多变量输入的LSTM神经网络的预测精度显著高于单变量LSTM神经网络和BP神经网络,预测均方根误差最小,仅为1.919 1。总体上,多变量LSTM神经网络地下水位预测模型仅在某些峰值处误差较大,但总体预测效果较为理想。 相似文献
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地基沉降预测模型和方法研究 总被引:3,自引:0,他引:3
地基沉降预测是岩土体稳定性研究的一项重要任务。基于邓英尔和Gompertz等已有预测模型的特点,提出一种新的地基沉降预测模型——邓英尔-Gompertz曲线模型,并提出了求解此类模型的新方法——规划求解法,解决多变量非线性方程的极值问题;同时探讨了测量点数目及其所处阶段对预测精度的影响,即:测量点越多预测效果越好。测量点处于发生阶段时预测结果一般偏小;测量点达到发展阶段时预测结果一般偏大;测量点达到成熟阶段时预测结果与预测模型、工程沉降特点有关;测量点达到极限阶段时预测结果符合实际情况。实例分析结果表明:与邓英尔、Gompertz等模型相比,新模型预测的结果更准确。 相似文献
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Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction. 相似文献
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矿产资源地球化学模型建立与定量预测研究以成矿成晕地质、地球化学理论为指导,以各尺度勘查地球化学数据为基础,以现代GIS信息技术为手段,通过研究总结典型矿田(矿集区)、矿床的异常特征,建立成矿带内典型矿床地球化学找矿模型,为预测区的地球化学定量预测提供类比依据,从而进行资源量预测。在资源量预测过程中加入了相似度、剥蚀程度、衬值等要素,有效地加大了预测靶区遴选的可信度,通过类比法与面金属量法两种预测方法的加权平均,使预测资源量合理。文章对草河掌—桓仁地区铜矿资源量进行了估算,共新增预测铜资源量21 812.9t。 相似文献
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针对煤层回采工作面顶板涌水量超前预测精度低,预测结果的时间空间概念不清,对生产实际的指导性不强等问题,从煤层回采的时空变化和地下水流演化过程入手,从理论上分析了以大井法、廊道法为代表的解析法预测工作面顶板涌水量存在的主要问题,提出了浅埋煤层回采过程中顶板含水层充水水量由脉动式静储水量释放与渐增式动态补给水量共同组成,并给出了随矿井采掘过程进行的渐进式矿井涌水量时空动态预测方法,不仅计算了全矿井涌水量的大小,也给出了涌水量的时间变化过程和空间分布特征。大大提高了预测结果的精度,对生产有实际指导意义。结合一实际矿井的采掘规划与生产接续计划,引进了新增水量、干扰水量及残余水量的概念,计算预测了矿井2011年-2015年生产过程中矿井涌水量及其动态变化过程。 相似文献
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The prediction of active landslide displacement is a critical component of an early warning system and helps prevent property damage and loss of human lives. For the colluvial landslides in the Three Gorges Reservoir, the monitored displacement, precipitation, and reservoir level indicated that the characteristics of the deformations were closely related to the seasonal fluctuation of rainfall and reservoir level and that the displacement curve versus time showed a stepwise pattern. Besides the geological conditions, landslide displacement also depended on the variation in the influencing factors. Two typical colluvial landslides, the Baishuihe landslide and the Bazimen landslide, were selected for case studies. To analyze the different response components of the total displacement, the accumulated displacement was divided into a trend and a periodic component using a time series model. For the prediction of the periodic displacement, a back-propagation neural network model was adopted with selected factors including (1) the accumulated precipitation during the last 1-month period, (2) the accumulated precipitation over a 2-month period, (3) change of reservoir level during the last 1 month, (4) the average elevation of the reservoir level in the current month, and (5) the accumulated displacement increment during 1 year. The prediction of the displacement showed a periodic response in the displacement as a function of the variation of the influencing factors. The prediction model provided a good representation of the measured slide displacement behavior at the Baishuihe and the Bazimen sites, which can be adopted for displacement prediction and early warning of colluvial landslides in the Three Gorges Reservoir. 相似文献
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Ankita Singh Nachiketa Acharya Uma Charan Mohanty Gopbandhu Mishra 《Comptes Rendus Geoscience》2013,345(2):62-72
The emerging advances in the field of dynamical prediction of monsoon using state-of-the-art General Circulation Models (GCMs) have led to the development of various multi model ensemble techniques (MMEs). In the present study, the concept of Canonical Correlation Analysis is used for making MME (referred as Multi Model Canonical Correlation Analysis or MMCCA) for the prediction of Indian summer monsoon rainfall (ISMR) during June-July-August-September (JJAS). This method has been employed on the rainfall outputs of six different GCMs for the period 1982 to 2008. The prediction skill of ISMR by MMCCA is compared with the simple composite method (SCM) (i.e. arithmetic mean of all GCMs), which is taken as a benchmark. After a rigorous analysis through different skill metrics such as correlation coefficient and index of agreement, the superiority of MMCCA over SCM is illustrated. Performance of both models is also evaluated during six typical monsoon years and the results indicate the potential of MMCCA over SCM in capturing the spatial pattern during extreme years. 相似文献
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径流序列的非线性和非平稳特性使得高精度的径流预报存在困难。本文组合EEMD和GRNN模型形成EEMD-GRNN耦合模型,预测时通过将径流序列分解为确定成分与随机成分并通过GRNN模型分别进行预测,预测值的加和则构成径流最终预测结果。EEMD-GRNN耦合模型应用到元江中上游,并与其他模型进行比较,结果表明:EEMD-GRNN耦合模型具有更高的预测精度,对径流的总体趋势预测有良好的效果,但在随机性的模拟上有待进一步完善。EEMD-GRNN耦合模型优于BP、GRNN、EEMD-BP模型,能有效提升径流预测的精度,可为流域的水资源优化调度等提供决策支持。 相似文献