黑河流域水资源实时调度系统

本文结合黑河流域实际情况和流域发展的现实需求，提出了“宏观总控、长短嵌套、实时决策、滚动修正”的流域水资源实时调度的模式，并应用自适应控制方法，建立了流域水资源的实时调度系统，包括相互嵌套的实时调度预报子系统、实时调度决策子系统和实时修正子系统。实时调度预报子系统主要包括长期和短期径流预报、土壤墒情预报和需水预报；实时调度决策子系统包括长期和短期调度决策；实时修正子系统是根据采集的实时信息，进行长期和短期调度的修正。     

基于SWAT模型的流域水环境管理及预报系统

基于SWAT模型的流域水环境管理及预报系统是一个以地理信息系统(GIS)和数据库管理系统为开发平台,通过与SWAT分布式水文模型的集成,面向管理和决策层的可视化动态信息系统。本系统利用SWAT分布式水文模型对水资源水环境进行综合评价和预报,并结合GIS技术实现了流域管理的可视化和辅助决策功能,实现流域水环境的信息管理与查询,来为管理部门提供有效、直观的决策信息。     

基于决策树算法的洪水预报模型

结合现有决策树技术的研究结果,在已有决策树方法的基础上,从积累的大量数据资料和信息反馈形成的水文相关数据库出发,基于决策树C4.5算法设计合理的计算流程来建立了洪水预报模型,并以预报太湖水位为例进行了实例研究.结果表明,基于决策树的洪水预报模型结构清晰,最终生成的预报规则简单明了,模型在率定期与检验期内均具有很高的精度...     

基于Web Service的城市水资源决策支持系统开发及应用

以Microsoft Visual Studio.NET 2003为主要开发工具,采用ASP.NET语言开发的基于Web Service技术的城市水资源决策支持系统,采用三层B/S结构模式,由信息管理子系统、需水预测子系统、可利用水分析子系统、水资源优化配置子系统和系统管理等5个子系统组成,可以满足城市水资源决策支持的功能要求,可以通过Internet远程访问和管理,界面友好,操作方便,具有很强的灵活性和可移植性。     

灌区水资源实时优化调度

根据实时调度原理及农业水资源配置特点,建立了分层耦合的水资源实时优化调度模型,该模型包括供需水中长期预测、中长期优化配置、供需水实时预报、短期渠系配水和基于模糊识别的实时修正5个模块,应用该模型可实现对预报、决策、实施、修正的滚动向前调度。在中长期优化调度模块中提出了利用模糊聚类和马尔科夫时间序列的预测方法对有效降水量进行预测,并根据预测的年型计算作物需水量和地下水可开采量。实例计算表明,应用该模型实行的实时优化调度与各种供水已知情况下的理想最优调度方案偏差较小。     

基于WebGIS的洪泽湖地区洪水预报预警系统

为了加强洪泽湖地区洪水风险管理,保障淮河下游防洪安全,采用WebGIS与水文学、水力学模型相结合,将一、二维耦合的水动力学模型内嵌于B/S系统进行洪水预报预警的方法。构建了一维洪泽湖地区河道、二维洪泽湖湖面及周边滞洪区相耦合的洪水演进模型,在此基础上开发了基于WebGIS的洪泽湖地区洪水预报预警系统,实现了对降雨、洪水进行实时监控、预报和对预警关注地点水位、流量变化、洪水淹没和超标洪水预警信息的可视化展示。系统进一步提升了洪泽湖地区的防洪数字化水平,可为避险减灾提供决策支持。     

基于模糊聚类预报与序贯决策的水资源开发利用总量动态管理模式

针对水资源开发利用总量控制红线在不同来水频率年的适应性管理问题,在模糊聚类理论以及水文预报的基础上,结合不确定性动态系统优化决策方法,提出基于径流聚类预报与用水量复核双向约束的时程滚动修正的序贯决策方法。通过对建立月来水频率预报模型,结合实际用水量对逐月指标进行滚动修正,实现"预报-复核"双向约束下逐月用水总量控制指标的动态决策。通过在我国南方地区的应用表明,该方法可以实现逐月来水的预判并制定控制指标,实现逐月用水的弹性管理,具有很好的实用价值。     

水库实时防洪调度方案快速生成模型研究

水库实时防洪调度存在不确定性信息,决策者往往需通过调整水位、泄量等约束以快速生成不同调度方案进行参考。针对该需求,首先建立了基于POA的防洪优化调度模型,使该模型具有较快的优化速度和较好的收敛性;其次将模型与考虑预报降雨级别的启发信息相结合,使计算结果符合泄流规律,具有更好适用性;最后细化模型的约束模式,使决策者能够方便地调整约束策略,增强了模型的交互性。以白山水库为工程背景,计算结果表明,该模型能够适应多变的决策要求快速生成可行方案,并有效降低最高水位、最大泄量,具有一定的参考性。     

Middle- and Long-Term Streamflow Forecasting and Uncertainty Analysis Using Lasso-DBN-Bootstrap Model

Middle-term and long-term streamflow forecasting is of great significance for water resources planning and management, cascade reservoirs optimal operation, agriculture and hydro-power generation. In this work, a framework was proposed which integrates least absolute shrinkage and selection operator (lasso), DBN and bootstrap to improve the performance and the stability of streamflow forecasting with the lead-time of one month. Lasso helps to screen the appropriate predictors for the DBN model, and the DBN model simulates the complex relationship between the selection predictors and streamflow, and then bootstrap with the DBN model contributes to evaluate the uncertainty. The Three-River Headwaters Region (TRHR) was taken as a case study. The results indicated that lasso-DBN-bootstrap model produced significantly more accurate forecasting results than the other three models and provides reliable information on the forecasting uncertainty, which will be valuable for water resources management and planning.

     

Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques

One of the key elements in achieving sustainable water resources and environmental management is forecasting the future condition of the surface water resources. In this study, the performance of a river flow forecasting model is improved when different input combinations and signal processing techniques are applied on multi‐layer backpropagation neural networks. Haar, Coiflet and Daubechies wavelet analysis are coupled with backpropagation neural networks model to develop hybrid wavelet neural networks models. Different models with different input selections and structures are developed for daily, weekly and monthly river flow forecasting in Ellen Brook River, Western Australia. Comparison of the performance of the hybrid approach with that of the original neural networks indicates that the hybrid models produce significantly better results. The improvement is more substantial for peak values and longer‐term forecasting, in which the Nash–Sutcliffe coefficient of efficiency for monthly river flow forecasting is improved from 0.63 to 0.89 in this study. Copyright © 2014 John Wiley & Sons, Ltd.     

长江流域防洪调度决策支持系统设计初探

为了提高长江流域防洪调度决策的时效性和可靠性,需构建一套服务于多层级用户的防汛管理智能决策支持体系。通过建立长江流域河网及控制性节点的拓扑关系,对核心业务功能进行了模块化设计,实现水情预报、洪水演进、防洪调度以及洪水风险分析的预报调度一体化。基于核心业务模型和信息扩展接口,利用GIS可视化技术及WebService交互技术的集成,研制了长江流域防洪调度决策支持系统,可实现流域水雨情的实时查询和防洪调度方案的快速模拟,为流域防汛管理决策提供专业、科学、准确的技术支持。     

Search beam method

An interactive multi‐objective decision‐making (MODM) model based on the Search Beam Method (SBM) and a finite‐difference groundwater flow model (GWM), is developed for analysing groundwater resources development and management problems. The SBM utilizes one‐dimensional search in the objective space to (systematically) identify the non‐dominated solutions. The steady‐state GWM determines the aquifer response due to well withdrawals. Three competing objectives of maximizing total yield, minimizing maximum compression and minimizing total pumping cost are considered. Applicability of the MODM model is demonstrated on a simplified confined aquifer system resembling one of the water‐bearing layers underlying Bangkok.     

基于BP神经网络的贝叶斯概率水文预报模型

本文在贝叶斯概率水文预报系统(BFS)框架之上，研究了双牌水库水文预报的不确定性，建立了流量先验分布及似然函数的BP神经网络模型，并通过Markov链Monte Carlo(MCMC)方法求解得到流量后验分布及其统计参数。通过对双牌水库历史洪水的研究结果表明，基于BP神经网络的BFS不仅显著提高了预报精度，而且为防洪决策提供了更多的信息，使得预报人员在决策中能考虑预报的不确定性，定量的估计各种决策的风险和后果。     

新安江模型在土壤墒情预报中的应用

近年来干旱已严重影响到自然生态环境的平衡及人类社会、经济的可持续发展。而墒情预报是干旱管理的一项基础工作。本文分析了国内外墒情预报的模型方法,并将新安江模型移用于土壤墒情的预报研究中,建立了基于水量平衡的土壤墒情预报方案。从模型的预测结果可以看出,模拟预报结果和实测值基本一致,该方法精度是较高的。新安江模型预报土壤含水量的模型结构简单,涉及参数少且物理意义明确,易于调试优选,便于在实际工作中推广使用。     

新安江模型在土壤墒情预报中的应用

近年来干旱已严重影响到自然生态环境的平衡及人类社会、经济的可待续发展。而墒情预报是干旱管理的一项基础工作。本文分析了国内外墒情预报的模型方法,将新安江模型移用于土壤墒情的预报研究中,建立了基于水量平衡的土壤墒情预报方案。从模型的预测结果可以看出,模拟预报结果和实测值基本一致,该方法精度是较高的。新安江模型预报土壤含水量的模型结构简单,涉及参数少且物理意义明确,易于调试优选,便于在实际工作中推广使用。     

浙东引水调度管理系统设计与实现

浙东引水工程输水线路长,运行管理十分复杂,为了保障浙东引水工程的长期安全稳定运行和工程引水调度效益的发挥,应用网络技术、数据库技术对引水线路沿程实时监测信息进行了汇聚和分析,提出了基于时间序列分析的引水调度的周期均值叠加预报的年计划预报方法、引水调度动态修正方法以及引水调度信息流程;研发了基于GIS的浙东引水调度管理系统。系统具有信息监测管理、调度管理、工程管理、政务服务等功能,实现了对引水调度信息的实时监测和汇聚共享。应用结果表明系统运行稳定,在工程引水调度中发挥了较好的作用。     

浙东引水调度管理系统设计与实现

浙东引水工程输水线路长,运行管理十分复杂,为了保障浙东引水工程的长期安全稳定运行和工程引水调度效益的发挥,应用网络技术、数据库技术对引水线路沿程实时监测信息进行了汇聚和分析,提出了基于时间序列分析的引水调度的周期均值叠加预报的年计划预报方法、引水调度动态修正方法以及引水调度信息流程;研发了基于GIS的浙东引水调度管理系统。系统具有信息监测管理、调度管理、工程管理、政务服务等功能,实现了对引水调度信息的实时监测和汇聚共享。应用结果表明系统运行稳定,在工程引水调度中发挥了较好的作用。     

基于气象模式的汉江流域洪水预报系统

针对现行洪水预报方法的不足与汉江流域的特点,建立了汉江流域MM5气象预报模式和VIC分布式水文模型,耦合集成3种预报模式并开发了基于气象模式的汉江流域洪水预报系统。应用2005~2007年汛期日水文气象资料进行验证,结果表明所建VIC分布式水文模型具有较高的模拟与预报精度。基于气象模式的汉江流域洪水预报系统可为汉江流域的中长期水文预报和水资源综合管理提供技术支撑和决策参考。     

Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand

Streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. Despite operational application of Ensemble Streamflow Prediction (ESP) for long-range streamflow forecasts in United States of America by the National Weather Service River Forecast System, no such approach has been explored in New Zealand. The objective of the present paper is to explore ESP-based forecasts in New Zealand catchments, highlighting its capability for seasonal flow forecasting. In this paper, a probabilistic forecast framework based on ESP technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. In the present study employs the ESP-based approach using the TopNet hydrological model with a range of past forcing data and current initial conditions. An ensemble stream flow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the Rangitata, Ahuriri, and Hooker and Jollie catchments in South Island, New Zealand. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23 to 69 % (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The ESP based forecast exhibited higher skill for a greater percentage of the forecasting period than climatology. As a result, the ESP forecast can provide better over all information for integrated water resources management purpose. ESP-based forecasts using the TopNet hydrological model have potential as tools for water resource management in New Zealand catchments.     

Short-Term Predictions of Hydrological Events on an Urbanized Watershed Using Supervised Classification

Many tasks of operational watershed management at the local level require stream flow predictions delivered to decision makers in a timely manner. In highly urbanized watersheds with an impermeable surface, stormwater runoff can cause rapid increases in water levels in streams leading to flood and even flash flood events. Usually, such rapid increases in water flow characteristics are predicted by process-based models with high levels of uncertainty. In this study, the prediction of magnitudes of the stream hydrological characteristics is replaced by the forecasting of an event (i.e., flood or no-flood) using data collected by stream and rain gauges at the watershed. The proposed approach is based on a black box model developed as an ensemble of classifiers generated by independent inducers to predict the class of a future hydrological event in a small highly urbanized watershed. Eight inducers were investigated in the phase space reconstructed from observation data using time-delay embedding extended to multiple observation sites. Five inducers were selected for the ensemble, where the final decision is made by majority vote. The developed model generates 45-minute and hourly predictions of high-flow events with more than 80 % precision – a threshold used in operational flood management. Model site-specific parameterization is replaced by the training step using observation data on water levels and precipitation which are collected at 15-minute intervals and are readily available. The proposed approach to developing a prediction tool can be used by local authorities as one of the methods for flood management.