Multi-Objective Parameter Calibration and Multi-Attribute Decision-Making: An Application to Conceptual Hydrological Model Calibration

For calibrating the conceptual hydrological models (CHM), the traditional calibration method with a single objective cannot properly measure all the behaviors of the hydrological system. To obtain a successful parameters calibration, in this paper, we propose a multi-objective cultural self-adaptive electromagnetism-like mechanism (MOCSEM) algorithm, which is first implemented in solving the parameters calibration problem of CHM. In this algorithm, a self-adaptive parameter is applied in local search operation for adjusting the values of parameters dynamically. Meanwhile, cultural algorithm (CA) is adopted to keep a good diversity and uniformity of Pareto-optimal solutions (POS). MOCSEM is tested, firstly, by several benchmark test problems. After achieving satisfactory performance on the test problems, a case study is implemented for parameter calibration of a CHM by comparing the properties of POS obtained by the MOCSEM and other methods. Finally, when the optimization problem quickly becomes a decision-making problem because of the multiple objectives in CHM, fuzzy technique for order preference by similarity to an ideal solution method has been used to rank the POS and select the optimal scheme. The results show that the MOCSEM algorithm can provide high-accuracy parameters of CHM on various decision-making scenarios.     

A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization

Practice experience suggests that the traditional calibration of hydrological models with single objective cannot properly measure all of the behaviors of the hydrological system. To circumvent this problem, in recent years, a lot of studies have looked into calibration of hydrological models with multi-objective. In this paper, we propose a novel multi-objective evolution algorithm entitled multi-objective shuffled complex differential evolution (MOSCDE) algorithm, which is an extension of the famous single objective algorithm, shuffled complex evolution (SCE-UA) algorithm, to the multi-objective framework. This new proposed algorithm replaces the simplex search used in SCE-UA with the differential evolution (DE) algorithm and can more thoroughly utilize the information of the individuals in the evolutionary population and improve the search ability of the algorithm. Meanwhile, the Cauchy mutation (CM) operator is employed to prevent the algorithm from falling into the local optimal region of the feasible space. Moreover, two types of archive sets are employed to further improve the performance of the algorithm. The efficacy of the MOSCDE algorithm is first tested on five benchmark problems. After achieving satisfactory performance on the test problems, the MOSCDE is applied to multi-objective parameter optimization of a hydrological model for daily runoff forecasting. The results show that the MOSCDE algorithm can be a viable alternative for multi-objective parameter optimization of hydrological model.     

An automatic parameter calibration method for the SWAT model in runoff simulation

Runoff simulation is highly significant for hydrological monitoring, flood peak simulation, water resource management, and basin protection. Runoff simulation by distributed hydrological models, such as the soil and water assessment tool (SWAT) model which is the most widely used, is becoming a hotspot for hydrological forecasting research. However, parameter calibration is inefficient and inaccurate for the SWAT model. An automatic parameter calibration (APC) method of the SWAT model was developed by hybrid of the genetic algorithm (GA) and particle swarm optimization (PSO). Multi‐station and multi‐period runoff simulation and accuracy analysis were conducted in the basin of the Zhangjiang River on the basis of this hybrid algorithm. For example, in the Yaoxiaba Station, the calibration results produced an R² of 0.87 and Nash Sutcliffe efficiency (NSE) index of 0.85, while verification results revealed an R² of 0.83 and NSE of 0.83. Results of this study show that the proposed method can effectively improve the efficiency and simulation accuracy of the model parameters. It can be concluded that the feasibility and applicability of GA‐PSO as an APC method for the SWAT model were confirmed via case studies. The proposed method can provide theoretical guidance for many hydrological research fields, such as hydrological simulation, flood prevention, and forecasting.     

A System Approach to Real-time Hydrologic Forecast in Watersheds

Abstract

In non-structural measurement of flood control, hydrologic forecasting plays a very important role. Owing to time-variance, non-linearity, and uncertainty of hydrological processes, realtime forecasting has become an efficient approach. The paper addresses an important practical problem: improving short-term hydrological forecasting based on real-time updating in the operation model. A simple nonlinear model with a variable gain parameter (VGPM) is developed. A separated calibration approach for updating parameters used in the runoff generation process and the response function in the flow routing is proposed. State space equations associated with updating model parameters in a real time scheme were developed. The VGPM approach is verified for three types of representative watersheds. The performances of different updating schemes in rainfall-runoff modeling and real-time forecasting were tested. The results indicate that significant improvement in the efficiency of hydrological modeling can be obtained from the VGPM approach, relative to simple linear models (SLM). For the watersheds with a time-variant characteristic, moreover, significant improvement in the hydrologic forecasting efficiency can be obtained by adaptive schemes. The efficiency of real-time modeling by the self-adaptive Kalman Filtering algorithm was found to be very close to that of the Recursive Least-Square method.     

基于多重演化模式重构的水文预报模型及应用

因长期受人类活动、气候变化等多重因素作用,水文时间序列表现出多时间尺度、多频率、动态变化、自记忆性等复杂性特征,增加了水文预报结果的不确定性。本文将经验模态分解模型,核主成分分析模型和支持向量机模型耦合,建立了针对复杂性水文时间序列的预报模型,并采用NASH效率系数、自相关系数、相对误差作为模拟预测精度及参数率定的多目标判断标准。模型应用于黄河花园口水文站径流序列的长期水文预报中,结果表明:模型预报时段长,具有较好的预测准确性和实践应用价值。该模型为多重因素作用的复杂性水文时间序列预报提供了一种方法。     

分布式水文模型EasyDHM模型

介绍了自主开发的分布式水文模型EasyDHM的空间单元离散方式、主要理论模块及相应的模型软件系统MWEasyDHM。EasyDHM模型的空间离散采用自主开发的通用子流域划分算法,很大程度上扩展了分布式水文模型的通用性,支持多种产汇流算法,支持用户对主要产汇流参数的敏感性分析和参数优化,以优化模型模拟效果,开发了专门的洪水预报模块,支持洪水实时校正及多模型组合预报技术并在嫩江流域使用EasyDHM模型进行了应用验证。     

Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models

The uncertainty research of hydrological forecast attracts the attention of a host of hydrological experts. Prediction Interval (PI) is a convinced method that can ensure the forecasting accuracy meanwhile take uncertainty range into consideration. While the existed Prediction Interval methods need algorithm optimization and are susceptible to local optima, so it is particularly urgent to provide an efficient Prediction Interval (PI) model with excellent performance. This paper proposes a novel upper and lower bound interval estimation model to rapidly define the PI and reduce the amount of calculation to implement convenient and high precise hydrological forecast. Above all, the ideal upper and lower bounds are defined according to the relative width or absolute width. Then, the proposed model is utilized to forecast interval runoff via least square method and multiple linear regression methods. The estimated interval inclusion ratio, interval width, symmetry, and root-mean-square error which are popular used to judge the precision serve as accuracy evaluation indexes. The measured discharge data from five hydrological stations which located upstream of the Yangtze River is applied for interval forecasting. Compared with the results of neural network-based upper and lower bound interval estimation model, the proposed method yields higher forecasting accuracy, meanwhile, the ideal upper and lower bounds successfully minimize the number of processes which require a mass of parameter searching and optimization.

     

连续API水文模型的研究及应用

连续API模型是传统的以前期影响雨量(Pa)为参数的降雨径流相关图法、单位线(或等时线)法与计算机技术相结合的产物,能预报连续的过程线.本文根据连续API模型原理,对模型参数进行改进,建立洪水预报模型应用于紧水滩流域,效果良好.     

基于数值预报模式的柘溪水库流域径流预报系统

水库的优化调度运行需要依赖预报准确率高、预见期长的径流预报结果。现有的柘溪水库流域径流预报系统由于未考虑水库流域未来时段的降水过程,径流预报的预见期和准确率无法满足汛期优化调度的要求。为此,在分析柘溪水库流域的水文和气候特征基础上,提出了数值预报模式和水文模型的耦合预报框架,设计了基于数值预报模式的柘溪水库流域径流预报系统架构,阐述了系统的主要功能与逻辑结构。通过系统的应用,可为提高汛期洪水的利用率、减少弃水、提高发电和防洪效益提供重要决策支撑。     

Impact of Distribution Type in Bayes Probability Flood Forecasting

Bayesian forecasting system (BFS) is widely applied to the hydrological forecast. Hydrological forecast processor (HUP), a key part of the Bayesian probability prediction, is conducted at the assumption that the rainfall is certain, which can simultaneously quantify the uncertainty of hydrological model and parameter. In the HUP, the runoff is usually assumed to obey Logweibull distribution or Normal distribution. However, Distribution type of the runoff is not certain at different areas, and there are few distribution types of HUP in existence. So common distribution types are needed to develop the HUP to provide an effective forecast result. In this paper, Nonparametric kernel density estimation, Pearson III and Empirical distribution were introduced as the prior distribution of HUP to eliminate the parameter uncertainty of probability density function. Also, the five distributions were compared in this study to get the diversity of distribution types and search the best appropriate distribution type. The 52 floods during 2004a-2014a of ZheXi basin are employed to calibrate and validate the different distribution types of BFS. The results show that the LogWeibull and Empirical Bayesian probabilistic model has the best performance on average results compared with the other four distribution models. Meanwhile the other distribution types proposed in this study have the similar ability on interval width and the containing rate of probability forecasting results. This demonstrates that more new distributions are required to make BFS more robust.     

枯水期径流量的中长期预估模式

为建立水文干旱预测系统，采用灰色系统理论中灰关联度分析的方法，对枯水期径流量的预估模式进行了探讨，并提出用层次分析法来考虑各影响因子对径流量的不同影响程度，这一尝试为水文干旱的预测提供了条件。     

径流长期分级预报的Kohonen网络方法

提出了径流长期分级预报一种新的模式识别方法。该方法不直接描述径流形成的物理机制，而是通过Ｋｏｈｏｎｅｎ自组织神经网络对历史样本（径流级别及其影响因子集）的学习，识别出蕴含在样本中径流级别与其影响因子之间的规律性。辽宁省大伙房水库基于气象因素的汛期径流预报实践，表明了该方法的可行性和有效性。     

基于多组群教学优化的随机森林预测模型及应用

为有效提高水文预测预报精度,提出了一种基于多组群教学优化(MGTLO)的随机森林(RF)预测方法,利用MGTLO算法对RF两个关键参数进行优化,构建MGTLO-RF预测模型,并与基于MGTLO算法优化的支持向量机(SVM)、BP神经网络两种常规预测模型作对比分析。以云南省龙潭站月径流和年径流预测为例进行实例研究,利用前44 a和后10 a资料对MGTLO-RF等3种模型进行训练和预测。结果表明:所提出的MGTLO-RF模型具有更好的预测精度和泛化能力,可作为水文预测预报和相关预测研究的一种有效工具。     

基于BP神经网络的MIKE SHE模型参数率定

为了更精细地对水文全过程进行描述和解析,更准确地构建分布式水文模型,以丹麦Karup流域为例,对MIKE SHE模型的饱和导水率、饱和带水平水力传导系数、河床透水系数进行了参数率定,模拟流域的日径流过程。结果表明:基于BP神经网络反分析的参数率定方法比MIKE SHE模型参数自动率定计算得到的均方根误差RMSE小,模型效率系数Ens更接近1;采用BP神经网络反演率定参数后,3组测试样本的日径流模拟过程的RMSE分别为0.04,0.03,0.08 m³/s,Ens均为0.99,且模拟结果能较好地反映径流的实际变化趋势。因此,这种基于BP神经网络反分析的参数率定方法对构建分布式水文模型具有一定的价值。     

黄河宁蒙河段冰情预报决策支持系统的设计与开发

以黄河宁蒙河段为例，在对河段历史冰情变化规律及其影响因素分析的基础上，建立冰情预报数据库，进行数据挖掘，并以地理信息系统（GIS）为平台，以水文学流量演算、热力学、冰水力学等原理为基础，结合相关经验预报模型，建立用实测资料进行参数率定的冰情预报概念性数学模型和人工神经网络模型，初步探讨了集信息查询、模型参数率定、气温预报、冰情预报等功能为一体的冰情预报决策支持系统的设计与开发。     

淮河鲁台子以上流域洪水预报模型研究

本文以淮河鲁台子以上流域为例,采用分布式概念性水文模型,对王家坝以上流域及阜阳、蒋家集、横排头淮干支流进行降雨径流与洪水过程研究,同时进行参数率定。采用马斯京根法、马斯京根水位模拟法和扩散波非线性水位法,对淮河干流王家坝至鲁台子区间具有行蓄洪区流域洪水进行预报。预报模型在2007年的大洪水预报调度中得到了检验,取得了较高的精度。     

基于PDM的飞来峡水库实时水文预报模型

水文预报模型是实时预报预警系统的核心。基于新型的概率水文预报PDM(The Probability Distributed Model),构建了北江流域飞来峡水库概率水文预报模型。将整个流域划分为18个子流域,对每个子流域分别建立PDM,并采用历史降雨流量数据序列对各子流域和流域PDM进行参数率定;以横石站为参考站,采用52场次实测历史洪水进行模型验证和精度评定,洪峰预报合格率为99%,精度等级为甲等,模拟精度高,说明了PDM结构和参数取值的合理性,以及模型在该流域的适用性。     

ARIMA与ANN组合预测模型在中长期径流预报中的应用

基于时间序列预测模型及BP神经网络,提出了新的组合预测方法.该方法采用三层结构的BP神经网络来构造组合预测模型,运用时间序列模型预测方法得出的预测结果,采用历史滚动法将前5年的预测结果数据作为BP网络的输入,以当前年份的预测结果为网络期望输入,建立了ARIMA-ANN组合预报模型.利用Matlab7神经网络工具箱对塔里木河上游源流卡群水文站的年径流量进行了预报及验证.结果表明:组合模型的预报结果精度高,容错能力强,是中长期径流预报的有效方法.     

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。     

AR模型在宝珠寺水库实时洪水预报校正中的应用

人类活动改变了流域下垫面,对洪水预报精度产生极大的影响,引进实时校正模型以提高洪水预报精度。根据宝珠寺水库的自然地理和水文气象特性,宝珠寺水库实时洪水预报采用新安江模型,实时校正模型采用时间序列AR模型。利用10年历史降雨径流资料,对新安江和实时校正模型的参数进行率定,并利用近年的2次洪水对模型进行检验,检验结果表明实时校正能明显地提高洪水预报的精度。