Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.     

急倾斜煤层开采地表沉陷的渐近灰色预测

煤层开采所引起的地表沉陷是一种严重的矿区地质灾害。煤层的倾角、厚度等物理条件是地表沉陷的主要影响因素。在水平煤层或缓倾斜煤的开采过程中，由于地层倾角小，地表沉陷具有较完整的规律性，其预测效果也比较理想。但是，在急倾斜煤层的开采中，由于地层倾角较大，赋存条件和地质体物理力学性质的差异，增强了地表下沉的非线性特征，使地表沉陷具有不确定的表现规律。文章对重庆市南桐矿区东林矿的地表沉陷非线性特征进行了探讨，得到了东林煤矿地表下沉曲线的分形维数是1．07。在岩层移动这个系统当中，既含有已知的又含有未知的或非确定的信息，可以作为一个灰色系统来研究。岩层控制系统的状态、结构和边界条件难以精确描述，属本征性灰色系统。文章针对东林煤矿地表下沉曲线非线性较弱的性质，提出用一种基于GM(1，1)的渐近预测模型对东林煤矿42个月的地表下沉量时间序列进行探讨。结果表明，这种模型对急倾斜层开采地表沉陷的预测是一种行之有效的方法。通过对其他工程实例的应用分析，进一步证明这种渐近的灰色预测方法具有相对较高的精度，是一种比较实用的地表沉陷预测方法，具有广泛的工程实用空间。     

人工神经网络方法预报长江上游流域面雨量的探讨

利用1998～2000年3～11月长江上游六大流域逐日降水实况和MAPS、T106(T213)、欧洲中心等数值产品资料，采用人工神经网络方法，建立了六大流域强降水面雨量等级预报模型。并于2002年6～9月进行了业务试验。     

舟山港台风风暴潮的分析及预报

利用 1 956～ 2 0 0 0年舟山市定海验潮站潮位观测资料 ,对历年影响舟山的台风风暴潮灾进行了全面的统计分析。将台风按路径分型 ,分析了不同路径台风风暴潮的特点 ,并用基于PRESS准则的逐步回归方法 ,建立了不同路径风暴潮的预报模型。     

地震资料曲流河辫状河油气储层识别与预测

曲流河、辫状河储层在我国的陆相盆地中广泛分布。由于曲流河相沉积砂体的单层薄、横向变化；辫状河相沉积的砂体迁移快、垂向加积等特点，增加了在勘探过程中对其分辨的难度。因此，对地震资料曲流河、辫状河油气储层的识别与预测，必须综合多种与其相关的地质资料，结合地震数据体中给出的信息来判断分析，以达到提高分辨率的目的。     

第二松花江流域水电厂综合中长期水文预报的分析研究

基于对第二松花江流域上游小山、松山、两江水电站中长期水文预报研究成果，论述了综合中长期水文预报的研究思路和定性预报、定量预报的分析研究方法。该研究通过2002年实践检验，具有较高的预报精度。     

通用洪水预报模型库设计建设研究

在介绍洪水预报模型库的基本概念和性质的基础上，分析了各类洪水预报模型的输入、输出数据类型，规定了各类数据文件格式，设计了洪水预报模型库的通用数据接口，并在中国洪水预报系统中实现了通用洪水预报模型库的建设。     

Probabilistic approach for active control of structures: experimental verification

In our previous study (Earthquake Engineering and Structural Dynamics 2003; 32 :2301), we have developed a probabilistic algorithm for active control of structures. In the probabilistic control algorithm, the control force is determined by the probability that the structural energy exceeds a specified target critical energy, and the direction of a control force is determined by the Lyapunov controller design method. In this paper, an experimental verification of the proposed probabilistic control algorithm is presented. A three‐story test structure equipped with an active mass driver (AMD) has been used. The effectiveness of the control algorithm has been examined by exciting the test structure using a sinusoidal signal, a scaled El Centro earthquake and a broadband Gaussian white noise; and, especially, experiments on control have been performed under different conditions to that of system identification in order to prove the stability and robustness of the proposed control algorithm. The experimental results indicate that the probabilistic control algorithm can achieve a significant response reduction under various types of ground excitations even when the modeling error exists. Copyright © 2004 John Wiley & Sons, Ltd.     

Model identification for hydrological forecasting under uncertainty

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.