大朝山水电站汛期水位动态控制运用研究

在水库调度运行中,汛期运用水位的确定是水库调度运行的重点和难点,它的正确与否直接关系到大坝安全及水库兴利.对大朝山水电站可行性研究和初步设计阶段确定的单一汛期运用水位作了介绍.对大朝山水库开展动态汛期运用水位研究的必要性及可行性进行了阐述.并利用旬设计洪水的计算理论与方法,结合大朝山水库实际调度运行情况,开展了大朝山水库近期动态汛期运用水位的研究.根据分析计算结果,确定了上游小湾水电站投产前大朝山水库汛期各旬运用水位,供水库调度运行中参考.     

对水库汛限水位动态控制风险分析的认识

水库汛限水位动态控制是缓解水库防洪与兴利矛盾的主要措施之一,水库汛限水位动态控制风险评估结果直接影响到水库汛限水位动态控制方案的选取。本文提出了水库汛限水位动态控制风险的概念,阐述了汛限水位动态控制风险评估的主要内容和进行风险分析计算采用的主要计算方法,提出了汛限水位动态控制风险评估的指标体系。     

水库实时调节防洪能力确定

水库实时防洪能力是指水库在一定的大坝安全工况和一定泄流能力条件下,当水库水位处于某一实时水位时,水库能够安全抵抗调节洪水量的大小.文中提出了防洪能力计算方法,为水库的防洪能力的确定提供了理论依据.     

水量平衡法在小湾水库调度中的应用

在水电站水库计算及调度中,水量平衡原理是一个最基本的观点。文章结合该原理在小湾水电站水库调度工作中实际运用的事例,说明在短期水位预测、反推入库流量进而修正水情遥测站点的不合理单点数据等方面的应用,证明了水量平衡原理在水电站水库调度工作中的指导意义。     

尾闾段河网一维水流数学模型应用研究

尾闾段河网常常是连接上下游的水上通航要道,且上游洪水需要通过尾闾段河网下泄,这就使得尾闾段河网水流运动的研究成为非常重要和十分必要的课题之一.在对尾闾段河网水流的运动特点及一维河网计算的研究现状评述的基础上,选用自编程序“河网水动力模型”和商用软件HEC-RAS分别建立一维河网水流模型,应用于赣江尾闾段,针对洪水流量、中水流量和枯水流量分别计算沿程水位.在洪水流量下,根据计算所得的水面线,找出各主要站点的水位作为警戒水位,当该站点水位有接近警戒水位的趋势时,提请有关部门应做好防洪工作.在中水期,主要考虑造床流量的影响.在枯水期,根据计算得出的水位,得到各主要站点的最低通航水位,为枯水期能否顺利通航提供依据.     

水库分级分期旱限水位设计与控制研究

水库旱限水位的合理设置与科学运用对实现水资源安全高效利用、减轻旱灾损失具有重要意义.针对现有方法确定旱限水位时未全面考虑用户对供水保证率、破坏深度的要求,借助优化方法计算而在推广应用时有较高的技术门槛与难度等问题,本文明确了水库分级分期旱限水位定义,以各用户供水保证率最大、最大供水破坏深度最小为目标,构建了旱限水位优化...     

基于无人智能技术的水库地形测量与库容计算研究

水库水位和库容通常对于水库调节径流能力具有重要的影响。依托山东省日照市陈家沟水库,开展无人智能技术的水库地形测量与库容计算研究。基于GPS测绘与无人测量船对陈家沟水库的水位-面积以及水位-库容进行研究,其中测量水下面积0.36 km²,陆上测量面积0.36 km²,合计测绘面积0.72 km²。在实测数据基础上,分别采用等高线容积法和DEM方格网法对水位-面积以及水位-库容进行分析计算。结果表明,两种计算方法得到的结果基本一致,基本与实际情况吻合,证明本文结果的合理性;水位-面积以及水位-库容相关关系满足线性正相关。等高线容积法方法简单,计算精度高,但工作繁琐、量大且只能人工完成;而DEM方格网法计算简单快捷,可由计算机自动完成,减小人工工作量,在未来的工程实际应用中具有较大优势。     

水库防洪限制水位分析与实例论证

该文概述了水库防洪限制水位的分析计算方法,并以官庄水库作为研究实例,分析论证了该水库的防洪限制水位,其分析论证方法对其他水库具有一定的借鉴指导作用。     

基于逆序递推的水库分级分期旱限水位确定方法研究

开展水库旱限水位确定技术研究,对于科学指导水库抗旱调度具有重要的意义。因此本文在旱限水位内涵解析的基础上,提出一种水库分级分期旱限水位计算新方法,充分考虑不同的干旱等级、不同行业年内需水过程的差异,采用逆序递推算法得到水库分级分期旱限水位。以山西省张峰水库作为研究对象,计算得到汛期、枯水期、农业灌溉期的旱警水位分别为 756.5 m,756.2 m,754.5 m,旱保水位分别为 735.8 m,730.7 m,728.2 m。通过方法对比分析,发现新的计算方法能够对更多的行业、更长的时段进行供水改善,在指导水库抗旱调度,提升水利工程群干旱防御能力方面具有显著优势。     

佛寺水库旱限水位确定方法的探讨

通过对水库旱限水位确定方法的探讨,分析确定了水库旱限水位.该指标是确定水库干旱预警等级的重要指标,是启动抗旱应急响应级别的重要依据.为水库控制运用、科学主动抗旱提供了依据.     

Improvement of Multi-Satellite Real-Time Precipitation Products for Ensemble Streamflow Simulation in a Middle Latitude Basin in South China

The real-time availability of several satellite-based precipitation products has recently provided hydrologists with an unprecedented opportunity to improve current hydrologic prediction capability for vast river basins, particularly for ungauged regions. However, the accuracy of real-time satellite precipitation data remains uncertain. This study aims to use three widely used real-time satellite precipitation products, namely, TRMM Multi satellite Precipitation Analysis real-time precipitation product 3B42 (TMPA 3B42RT), Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIAN), and NOAA/Climate Precipitation Center Morphing Technique (CMORPH), for ensemble stream flow simulation with the gridded xinanjiang (XAJ) model and shuffled complex evolution metropolis (SCEM-UA) algorithm in the middle-latitude Mishui basin in South China. To account the bias of the satellite precipitation data and consider the input uncertainty, two different methods, i.e. a precipitation error multiplier and a precipitation error model were introduced. For each precipitation input model, the posterior probability distribution of the parameters and their associated uncertainty were calibrated using the SCEM-UA algorithm, and 15,000 ensemble stream flow simulations were conducted. The simulations of the satellite precipitation data were then optimally merged using the Bayseian model averaging (BMA) method. The result shows that in Mishui basin, the three sets of real-time satellite precipitation data largely underestimated rainfall. Streamflow simulation performed poorly when the raw satellite precipitation data were taken as input and the model parameters were calibrated with gauged data. By implementing the precipitation error multiplier and the precipitation error model and then recalibrating the model, the behavior of the simulated stream flow and calculated uncertainty boundary were significantly improved. Furthermore, the BMA combination of the simulations from the three datasets resulted in a significantly better prediction with a remarkably reliable uncertainty boundary and was comparable with the simulation using the post-real-time bias-corrected research quality TMPA 3B42V7. The proposed methodology of bias adjustment, uncertainty analysis, and BMA combination collectively facilitates the application of the current three real-time satellite data to hydrological prediction and water resource management over many under-gauged basins. This research is also an investigation on the hydrological utility of multi-satellite precipitation data ensembles, which can potentially integrate additional more satellite products when the Global Precipitation Measuring mission with 9-satellite constellation is anticipated in 2014.     

基于 Embedding-GRU 的水库水位预测模型

为解决因水库数据采集设备能力有限、水文数据不全导致预测水库水位时预测精度较低的问题，以四岭水 库每小时水位监测数据为例，提出基于嵌入式-门控循环单元（Embedding-gated?recurrent?unit，Embedding-GRU）的 水库水位预测模型，即利用 Embedding 方法将单维降雨量数据升维至多维数据，扩大降雨的气候特征，结合 GRU 算法进行水库水位预测。将该模型与传统深度学习算法长短期记忆（long?short-term?memory，LSTM）、门控循环单 元（gated?recurrent?unit，GRU）、双向门控循环单元（bidirectional?recurrent?neural?network，BiGRU）这 3 种模型对比， 结果显示：Embedding-GRU 模型的预测效果均优于其他传统模型，平均绝对误差 EMA和均方根误差 ERMS分别平均 下降 19.6% 和 7.7%，并且在预测次日水库水位的应用场景中决定系数 R2能够达到 0.989?37。结果表明：该模型耦 合多种算法，扩大单变量的气候特征，具有较高预测精度和泛化能力。相较传统模型，基于 Embedding-GRU 的水 库水位预测模型能够对缺少温度、气压、风速、蒸发量等监测数据的水库进行可靠度较高的预测，适用水库范围 更广，为水库日常运维、除险加固提供参考。     

ARIMA-SVR组合方法在水质预测中的应用

针对复杂水域水质变化机理难以掌握、水质预测建模困难且预测精度低的问题,将时间序列分析方法与机器学习方法引入水质预测领域,提出了基于差分自回归移动平均(ARIMA)与支持向量回归(SVR)组合模型的水质预测方法。数据经过预处理后先由ARIMA模型对其进行线性拟合,然后通过SVR模型预测残差以补偿其中的非线性变化。选择巢湖水域2004—2015年间的pH和溶解氧监测数据作为试验样本,通过Hodrick-Prescott(HP)滤波方法分析,结果表明2组数据具有不同的趋势特性和波动特性。根据精度评价指标对比分析模型的预测效果,结果表明组合模型预测精度显著提高,pH和溶解氧预测值与观测值间的相关系数均达到了0.99,均方根误差分别为0.20和0.61,平均绝对百分比误差分别为2.2%和6.6%。本研究所建立的组合预测方法具有较高的预测精度和较强的泛化能力,适用于复杂水域的水质预测。     

非弃水期葛洲坝水电站下游水位变化过程预测新方法

由于现有的水电站下游水位预测方法计算误差较大,选择葛洲坝水电站为研究对象,提出一种新的电站在不弃水情况下的下游水位变化过程预测方法。该方法基于BP神经网络算法,利用水电站监控数据实现电站下游水位的高精度预测,满足电站实时调度需求。对比现有的水位流量曲线查值法和非恒定流经验公式法,该方法有如下优势:①无需采用出库流量进行预测,避免了流量计算误差的影响;②建模过程中考虑了下游水位变化"后效性"影响,大幅提升水电站调峰时的预测精度;③可直接计算下游水位变化过程,计算结果稳定,精度更高,尤其在非弃水期葛洲坝水电站大调峰工况下,预测精度明显提高。     

多声路时差法在牛栏江—滇池补水工程流量自动监测中的应用

为解决牛栏江—滇池补水工程调水水量在线监测问题,依据超声波时差法的方法原理,结合牛栏江—滇池补水工程调水末端测验断面水文特性,采用多声路超声波传感器等设备实现前端数据采集,经超声波时差法流量模型计算实时流量,再通过GPRS信道传送到中心站,对数据进行分析处理、存储、转录、发布,实现WEB远程实时流量、工作状态等查询功能。经两年多的运行,系统正常稳定,其监测精度满足一类精度站要求,可为河渠人工规则断面流量自动监测提供参考。     

Prediction and simulation of monthly groundwater levels by genetic programming

Groundwater level is an effective parameter in the determination of accuracy in groundwater modeling. Thus, application of simple tools to predict future groundwater levels and fill-in gaps in data sets are important issues in groundwater hydrology. Prediction and simulation are two approaches that use previous and previous-current data sets to complete time series. Artificial intelligence is a computing method that is capable to predict and simulate different system states without using complex relations. This paper investigates the capability of an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as two artificial intelligence tools to predict and simulate groundwater levels in three observation wells in the Karaj plain of Iran. Precipitation and evaporation from a surface water body and water levels in observation wells penetrating an aquifer system are used to fill-in gaps in data sets and estimate monthly groundwater level series. Results show that GP decreases the average value of root mean squared error (RMSE) as the error criterion for the observation wells in the training and testing data sets 8.35 and 11.33 percent, respectively, compared to the average of RMSE by ANFIS in prediction. Similarly, the average value of RMSE for different observation wells used in simulation improves the accuracy of prediction 9.89 and 8.40 percent in the training and testing data sets, respectively. These results indicate that the proposed prediction and simulation approach, based on GP, is an effective tool in determining groundwater levels.     

猴子岩水电站深孔泄洪洞水力学试验及数值模拟

深孔泄洪洞是猴子岩水电站4套泄洪设施之一,其落差超过100m,洞内泄流流速最大可达22.5m/s,有高水头大流速这一特点。本文采用双方程紊流模型及基于水气两相流的VOF方法,对猴子岩深孔泄洪洞有压洞弯段及无压洞段的多种水力学要素进行三维数值模拟。数值模拟结果与1:25单体模型试验数据对比表明,采用该紊流模型与数值计算方法,能够很好地模拟这种高水头、大流量且带有自由表面的掺气水流的水力特性。计算模拟出的多种水力特性,从变化趋势到数值精度可满足水工设计的要求。     

Study on Water Quality Prediction of Urban Reservoir by Coupled CEEMDAN Decomposition and LSTM Neural Network Model

Urban reservoir is one of the important urban drinking water sources, and it is of important significance to ensuring the safety of urban water supply. The water quality of the reservoir is an important factor affecting the safety of water supply. Timely and accurate water quality prediction is very important for the formulation of a scientific and reasonable reservoir water supply plan. Considering the problem of high requirement of basic data in constructing water quality hydrodynamic physical model, this paper established a new data-driven model of water quality prediction in urban reservoir based on the Long and Short-Term Memory (LSTM) model, and the water quality data’s decomposition is implemented through the Complete Ensemble Empirical Modal Decomposition with Adaptive Noise (CEEMDAN) method. This model can not only realize the water quality prediction during different foreseen periods, but also solve the problem of low prediction accuracy caused by the randomness and large volatility of the measured data. Taking Xili Reservoir in Shenzhen of China as an example, the prediction of water concentration including total nitrogen, ammonia nitrogen, total phosphorus and PH value of Xili reservoir was realized based on historical monitoring data. Through simulation calculation, the prediction results of total nitrogen, ammonia nitrogen, total phosphorus and PH value in the water quality prediction model are highly consistent with the measured results, it is found that the simulation effect is good, and this model can well simulate the reservoir’s water quality concentration change process. For the total nitrogen and ammonia nitrogen, the relative prediction error of the model can be controlled below 10%, which shows the rationality of the built model. The research of this paper can provide an important theoretical and technical support for the water quality prediction and operation plan formulation of Xili Reservoir.

     

基于改进 GRA-NARX 模型的不同预见期 泵站水位预测

基于超参数自动率定的 GRA-NARX（grey?relation?analysis-nonlinear?autoregressive?model?with?exogenous?inputs） 模型是 GRA-NARX 模型的一种有效改进。以南水北调东线一期工程洪泽泵站为例，使用基于超参数自动率定的 GRA-NARX 模型，针对 1?h 和 2?h 时间间隔的输入数据，分别预测 3 个短预见期（2?h、4?h、6?h）和 1 个长预见期（12?h） 的泵站站前水位，并与 GRA-BP（grey?relation?analysis-back?propagation）模型的预测结果进行比较。结果表明：不同 预见期（2?h、4?h、6?h、12?h）下，基于超参数自动率定的 GRA-NARX 模型的相关系数、均方根误差和平均绝对误差 等指标均相差不大，预测精度高，且皆优于 GRA-BP 模型；采用 1?h 时间间隔的输入数据预测结果优于 2?h 时间间 隔的输入数据结果。研究成果可为不同预见期泵站站前水位预测提供理论参考。     

远程分布式地下水位监测系统

地下水、水库等水位的变化情况直接和人类生活息息相关。目前一般采用水尺或水位计进行水位测定,很多时候只能在现场进行人工水位测量,往往造成观测结果不准确等问题。为解决人工操作带来的不利影响,通过采用嵌入式系统搭建B/S架构的远程水位监测系统,能实现24 h水位实时监测和数据存储,可远程进行数据下载和查看,系统测量分辨率可达1 mm,测量精度在2 cm以内,既可用于单点的监测,也可用于多点区域性分布式监测。经试验证明,该水位监测系统能实现长期稳定的水位观测,能满足大部分水位监测领域的需求。