Incorporating multiple observations for distributed hydrologic model calibration: An approach using a multi-objective evolutionary algorithm and clustering

The use of distributed data for model calibration is becoming more popular in the advent of the availability of spatially distributed observations. Hydrological model calibration has traditionally been carried out using single objective optimisation and only recently has been extended to a multi-objective optimisation domain. By formulating the calibration problem with several objectives, each objective relating to a set of observations, the parameter sets can be constrained more effectively. However, many previous multi-objective calibration studies do not consider individual observations or catchment responses separately, but instead utilises some form of aggregation of objectives. This paper proposes a multi-objective calibration approach that can efficiently handle many objectives using both clustering and preference ordered ranking. The algorithm is applied to calibrate the MIKE SHE distributed hydrologic model and tested on the Karup catchment in Denmark. The results indicate that the preferred solutions selected using the proposed algorithm are good compromise solutions and the parameter values are well defined. Clustering with Kohonen mapping was able to reduce the number of objective functions from 18 to 5. Calibration using the standard deviation of groundwater level residuals enabled us to identify a group of wells that may not be simulated properly, thus highlighting potential problems with the model parameterisation.     

遗传算法及其在图像阈值选取中的应用

论述了遗传算法的基本原理、操作和特点,并以图像阈值选取中的最大类间方差法为例,给出用遗传算法优化图像阈值选取的步骤和方法,实验结果表明了这一方法的有效性。     

Application of variational algorithms in semi- lagrangian framework

1.IntroductionWeatherforecastingmodelisasetofpartialdifferentialequations(PDE).Itshouldsatisfycertaininitial--boundaryconditions.Clearly,oneofthemostimportantimprovementsinweatherforecastingmodelistooptimizeitsinitialandboundaryconditions.Forthesimplicityofdiscussion,wemightregardthespatialboundaryconditionasakindofinitialconditioninspatialdimension.Aneffectivedataassimilationprocedureisrequiredinordertoutilizewidelycollectivedatafortheweatherforecastingmodelandfindanoptimalinitialcondition.…     

Bayesian inversion with Markov chains—I. The magnetotelluricone-dimensional case

We use Monte Carlo Markov chains to solve the Bayesian MT inverse problem in layered situations. The domain under study is divided into homogeneous layers, and the model parameters are the conductivity of each layer. We use an a priori distribution of the parameters which favours smooth models. For each layer, the a priori and a posteriori distributions are digitized over a limited set of conductivity values.
  The Markov chain relies on updating the model parameters during successive scanning of the domain under study. For each step of the scanning, the conductivity is updated in one layer given the actual value of the conductivity in the other layers. Thus we designed an ergodic Markov chain, the invariant distribution of which is the a posteriori distribution of the parameters, provided the forward problem is completely solved at each step.
  We have estimated the a posteriori marginal probability distributions from the simulated successive values of the Markov chain. In addition, we give examples of complex magnetotelluric impedance inversion in tabular situations, for both synthetic models and field situations, and discuss the influence of the smoothing parameter.     

基于遗传算法的分类体系在地震预报中的应用探索

采用模似自然界生物进化过程的遗传算法的分类体系，并以首都圈地区为例，利用地震活动性资料的8种时空参数在地震预报中进行了应用探索．通过对1967-1992年间信息的学习演化，获得了20条拥有不同适应度的具有层次结构的地震预报规则集．对首都圈地区中等地震活动的预报规则进行了初步的分析讨论，发现了人们以往难于考虑周全的可能具有重要预报意义的异常组配.应用探索表明：在地震预报研究中应用具有自适应和潜在学习能力的基于遗传算法的分类体系，将可能推动人们对地震前兆及其物理机制的研究和探讨．     

基于快速非支配排序遗传算法的阻尼器多目标优化布置

将快速非支配排序遗传算法(NSGA-Ⅱ)和并行遗传算法相结合,提出内嵌NSGA-Ⅱ的粗粒度-主从式并行遗传算法。该算法将种群分为多个子种群,每个子种群可独立并行执行NSGA-Ⅱ操作;达到迁移周期时,子种群之间执行迁移操作;完成迁移后,子种群再次独立并行执行NSGA-Ⅱ操作。以最大层间位移角和最大楼层加速度为目标函数,对14层消能减震钢框架结构上的阻尼器布置位置进行优化分析。结果表明：该算法既实现多目标优化,又提高优化速度;对比常规隔层布置方法,该算法可使结构的层间位移角减震系数和加速度减震系数分别至少提高16.82%和16.01%。     

黄海、东海水体总吸收系数光谱特性及其统计反演模式研究

利用现场实测的表观光学量和固有光学量数据,得到了我国黄海、东海近岸二类水体多个波段的总吸收系数的统计反演模式。此反演模式采用412/555、490/555两个波段遥感反射比比值的二项式,得到波长412、440、488、510、532、555nm处的总吸收系数,其反演值和实测值的平均相对误差不大于25.8%,相关系数R2达到了0.75到0.85。水体总吸收系数几种统计模型的误差敏感性分析表明,反演模式对±5%的遥感反射比输入误差导致结果增加误差最大为24.0%,因此反演模式是可用的。同时给出了412、488、510、532、555nm各波段的总吸收系数同波段440nm的总吸收系数之间的关系。结果表明,在400—600nm波段范围内,每一个波段的总吸收系数与440nm波段的总吸收系数的相关性均较高,相关系数R2都超过了0.99。通过对拟合直线的斜率与波长进行回归,得到斜率和波长的关系,其相关系数R2为0.99,这样利用本文中建立的各波段总吸收系数关系模型,可以从一个已知波段的总吸收系数反演出任何另外一个波段的总吸收系数,这就在水色反演与应用中大大减少了未知因子的个数。     

几种卫星合成孔径雷达影像配准算法的比较研究

复数影像配准是卫星合成孔径雷达干涉数据处理中的重要步骤之一,影像配准精度将直接影响干涉条纹图的质量和由此提取的高程或位移结果的精度。文中利用上海地区ERS-1/2影像数据进行实验,对比分析了三种方法的配准质量和运算效率,试验结果表明:在配准质量方面,相干系数法最高,相位差影像平均波动函数法次之;而在信噪比较低的情况下,最大频谱法的配准质量优于相位差影像平均波动函数法。在运算效率方面,相位差影像平均波动函数法的运算效率最高。     

一种启发式A*算法和网格划分的空间可达性计算方法

本文提出了一个适用于研究城市内部的个体或商业区位的微观可达性计算方法,该方法的核心是将研究区域进行等距的网格划分,通过计算每个网格的可达性指标,来研究整个区域的可达性的空间分布特点。在可达性计算中,利用网格内的道路密度和土地利用状态这两个因素来模拟计算每个网格的交通成本,引入了启发式A ^*空间搜索算法来计算网格间路径的交通成本,并且加入适当的启发信息,提高了搜索效率,使搜索结果更符合实际需求。最后,基于本文提出的方法,利用GIS二次开发工具ArcEngine开发了计算程序,收集了多源数据,以广州市商务区的可达性作为计算对象,进行了商务区的可达性和易达性案例计算。     

一种CMP道集剩余校正量求取算法

地震资料处理中CMP道集在经过静校正和动校正处理之后,仍然会存在剩余校正量。剩余校正量的存在影响叠加成像效果。传统剩余校正量求取采用基于统计规律的互相关算法。设计了一种振幅绝对误差剩余校正量求取算法,并用MATLAB编程进行了仿真测试和实际地震资料处理试验。两种算法有着相同的效果,但振幅绝对误差算法速度更快。