天生桥一级水电站月平均入库流量逐月滚动预测模型

由于江河流域的月平均流量变化与月尺度的短期气候变化密切相关,因此作为一种尝试,将月尺度短期气候变化的逐月滚动预测模型直接用于江河流域的月平均流量预测。文中从短期气候变化的逐月滚动预测模型出发,建立了天生桥一级水电站月平均入库流量的一种逐月滚动预测模型,并用该模型对天生桥一级水电站2004年1月以后的月平均入库流量进行预测,取得了较为满意的效果。     

A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming

Water Resources Management - Precipitation is regarded as the basic component of the global hydrological cycle. This study develops a recursive approach to long-term prediction of monthly...     

Correction to: A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming

Water Resources Management - The article A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming, written by Suning Liu and Haiyun Shi was originally...     

自回归模型在月径流过程概率预报中的应用

把自回归模型用于月径流过程概率预报中。首先根据历史径流的概率分布利用自回归模型预报出一个概率，再根据这个预报出的概率进行径流概率预报。并把这种方法用于三峡电站的入库径流预报中，取得了较好的效果。     

月水量平衡模型的参数率定及组合预报

以两参数月水量平衡模型为研究对象,针对单目标参数率定的不足,建立了基于MOSCEM-UA算法的模糊多目标参数率定方法。该方法能够有效搜寻非劣参数集,进而在考虑不同目标偏好的条件下给出"满意参数"。针对单一模型模拟精度难以提高的局限性,将月水量平衡模型与人工神经网络模型(ANN)进行加权组合建立最优组合预报模型。实例表明:所建立的模糊多目标参数率定方法是有效的,与单一模型相比,组合模型能够提高模拟精度。     

逐步回归和BP神经网络模型的枯季月径流预测

枯季径流是工农业用水的重要来源,分析和预报流域枯季来水情况,可为科学制定用水方案、合理调配水资源提供依据。运用逐步回归模型和BP神经网络模型分别对盘龙河流域枯季月径流进行拟合和预报分析,并采用相关系数、相对误差、合格率对两个模型预测精度进行比较。结果表明BP神经网络模型预测精度更高,预测结果精度满足规范要求,更适用于盘龙河流域枯期月径流的预测。     

基于GM-SVR耦合的月径流预测模型研究与应用

考虑到某月径流与该月历史同期径流以及临近月径流均有较强相关性,而通常预报方法只采用其中一种径流序列,导致了可用信息损失。为此,提出一种基于灰色理论和支持向量机回归的组合预报模型。提出的模型综合利用了径流年内变化和年际变化信息,与单一灰色模型和支持向量机模型进行预测对比,结果表明基于灰色支持向量机的月径流模型预测精度明显高于单一模型,尤其是对径流变化剧烈的汛期表现出更优越的预测性能。     

最近邻抽样回归模型及其在枯水期月径流预报中的应用

为合理调配水资源,做好枯季径流预报,可采用最近邻抽样回归模型进行预测。按照最近邻抽样回归模型的基本思路和实现算法,根据长江上游主要控制站——寸滩站1893年1月—2009年12月历史同期月整编资料,对该流域的枯季径流特性进行分析研究,通过建立模型,对模型预测效果进行验证。结果表明：该模型对枯季径流的预报精度较高,可用于作业预报。     

Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. The honey-bees mating process may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bees mating. In this paper, the honey-bees mating optimization algorithm (HBMO) is presented and tested with few benchmark examples consisting of highly non-linear constrained and/or unconstrained real-valued mathematical models. The performance of the algorithm is quite comparable with the results of the well-developed genetic algorithm. The HBMO algorithm is also applied to the operation of a single reservoir with 60 periods with the objective of minimizing the total square deviation from target demands. Results obtained are promising and compare well with the results of other well-known heuristic approaches.     

灰色GM(1,1)模型在干旱预测中的应用

灰色系统理论及其建模原理,利用蚌埠市气象站27a的实测降水量资料建立灰色预测GM(1,1)模型,对干旱灾害进行预测,经残差与后验差检验分析,模型精度较高,平均达99.37%,使用实测资料检验,效果较理想,为蚌埠市抗旱及供水提供必要的预测信息。     

A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Water Resources Management - In recent decades, due to groundwater withdrawal in the Kabodarahang region, Iran, Hamadan, hazardous events such as sinkholes, droughts, water scarcity, etc., have...     

A New Framework Based on Data-Based Mechanistic Model and Forgetting Mechanism for Flood Forecast

Water Resources Management - The classification and identification can increase the prediction accuracy effectively due to the complexity and regularity of flood formation. However, it is difficult...     

对数马尔可夫模型在乌鲁木齐市降水预测中的应用

中长期降水量的预测是气象科学的一个难点问题,也是水文学中的一个重要问题。建立对数马尔可夫模型预测降水量,弥补了传统的马尔可夫模型降水预测中峰值的不准确性,提高了预测精度,并用乌鲁木齐市气象站43年降水资料进行了验证。结果表明,模型预测精度较高,为干旱半干旱区中长期降水量预报提供了一条简便可行的途径。     

新丰江水库月径流长期预报方法研究

研究新丰江水库月径流长期预报方法，根据7种预报因子，建立7个预报方案，将不同预报方案的预报结果进行预报集成和预报决策，从而获得定量的月径流预报值。实测资料检验结果表明：方法可行，预报精度令人满意，可应用于生产实践。     

GM(1,1)模型在大坝沉降预测中的应用

针对某水库大坝实际情况,提出在现有沉降观测数据的基础上,运用灰色理论的方法,建立GM(1,1)模型。通过数值试验确定模型最佳维数后,采取淘汰过于陈旧的数据信息、补充新观测数据的方法,即建立GM(1,1)等维新息模型,用以预测该大坝的沉降量,分析结果表明:GM(1,1)等维新息模型能较好地预测该大坝的沉降发展趋势。     

降水径流特征变化对月水量平衡模型模拟精度及参数的影响

为评估水文模型在变化环境下的可靠性,以两参数月水量平衡模型为例,采用Mann-Kendall法分析降水径流资料在MOPEX数据集中美国本土的104个典型流域的一致性,基于可变模糊理论评价降水径流资料一致性对两参数月水量平衡模型模拟精度的影响,并探讨降水径流资料一致性和流域的气候特征对模型参数的影响。结果表明:研究流域中,92.31%流域的降水或径流资料的一致性遭到破坏;通过可变模糊集评价模型模拟效果,发现降水、径流趋势变化均会削弱水文模型的模拟能力,其中降水一致性破坏是水文模型模拟能力减弱的主要原因,并且该方法能够准确地识别影响模拟能力的次要因素;模型参数C和参数SC均随着流域多年平均径流系数增大而增大,参数C反映了流域的湿润程度,参数SC表征了流域的调蓄能力。研究成果可为防洪、抗旱、水资源规划和管理提供技术支撑。     

A New Method for Estimation the Regional Precipitation

In this article we propose a new method - the Most Probable Precipitation Method (MPPM) - for estimating the precipitation at regional scale. Comparisons with the Thiessen polygons methods (TPM), inverse distance weighting interpolation (IDW) and ordinary kriging (OK) on annual, monthly, seasonal and annual maximum monthly precipitation are provided. In all cases MPPM performs better than IDW and OK, and in most of them, better than TPM.     

New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization

Multiple studies have developed management models to identify optimal operating policies for reservoirs in the last four decades. In an uncertain environment, in which climatic factors such as stream flow are stochastic, the economic returns from reservoir releases that are based on policy are uncertain. Furthermore, the consequences of reservoir release are not fully realized until it occurs. Rather than explicitly recognizing the full spectrum of consequences that are possible within an uncertain environment, the existing optimization models have focused on addressing these uncertainties by identifying the release policies that optimize the summative metric of the risks that are associated with release decisions. This technique has limitations for representing risks that are associated with release policy decisions. In fact, the approach of these techniques may conflict with the actual attitudes of the decision-makers regarding the risk aspects of release policies. The risk aspects of these decisions affect the design and operation of multi-purpose reservoirs. A method is needed to completely represent and evaluate potential consequences that are associated with release decisions. In this study, these techniques were reviewed from the stochastic model and risk analysis perspectives. Therefore, previously developed optimization models for operating dams and reservoirs were reviewed based on their advantages and disadvantages. Specifically, optimal release decisions that use the stochastic variable impacts and the levels of risk that are associated with decisions were evaluated regarding model performance. In addition, a new approach was introduced to develop an optimization model that is capable of replicating the manner in which reservoir release decision risks are perceived and interpreted. This model is based on the Neural Network (NN) theory and enables a more complete representation of the risk function that occurs from particular reservoir release decisions.     

一种新型耦合模型在月径流预测中的应用

基于投影寻踪分析与随机分析提出了一种新的耦合预测模型,运用投影寻踪技术将年内12个月径流由遗传算法优化得投影值,获取投影值与年径流的相关关系;建立年径流预测模型,由预测的年径流推算对应的投影值z^;寻找与z^最近邻的h个模式,由最近邻回归进行年内月径流展望预测。将耦合模型应用于宝珠寺和三峡水电站入库月径流展望预测,结果表明该耦合模型可行且预测效果较好。     

A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data

Researchers have studied to forecast the streamflow in order to develop the water usage policy. They have used not only traditional methods, but also computer aided methods. Some black-box models, like Adaptive Neuro Fuzzy Inference Systems (ANFIS), became very popular for the hydrologic engineering, because of their rapidity and less variation requirements. Wavelet Transform has become a useful tool for the analysis of the variations in time series. In this study, a hybrid model, Wavelet-Neuro Fuzzy (WNF), has been used to forecast the streamflow data of 5 Flow Observation Stations (FOS), which belong to Sakarya Basin in Turkey. In order to evaluate the accuracy performance of the model, Auto Regressive Integrated Moving Average (ARIMA) model has been used with the same data sets. The comparison has been made by Root Mean Squared Errors (RMSE) of the models. Results showed that hybrid WNF model forecasts the streamflow more accurately than ARIMA model.