正交化方法在水质模型参数确定中的应用

本文根据常微分方程参数反问题的数学理论，将正交化方法同有限差分法结合用于确定水质模型参数，并与正则化方法、最速下降法和共轭梯度法作了比较。其计算结果对比表明，正交化方法具有快速、简便、可靠的特点。更适合于水质模型参数的确定。     

弹上火工品可靠性评估方法分析

准确评估弹上火工品可靠度,对掌握弹上火工品可靠性水平和全弹可靠性评估起着至关重要的作用。本文根据弹上火工品试验数据特点,将可靠性评估方法归纳为成败型和连续型。针对成败型数据特点,研究了基于大样本量的二项分布法和基于小子样的最大熵试验法。针对连续型数据的特点,研究了应力—强度干涉模型中单一参数服从正态分布且其他参数为定值的计量法,双参数服从正态分布的应力—强度干涉模型及基于基准设计许用值和可靠性的安全系数法。通过分析上述方法的特点,介绍了可靠性评估方法的应用范围。     

改进的属性识别模型在湖泊富营养化评价中的应用

随着经济发展,水污染日益加剧,水体富营养化问题目益突出.将改进的属性识别模型结合熵权法对新疆艾比湖的富营养化状况进行了评价,并与综合营养状态指数法和改进密切值法进行比较,结果吻合.该方法计算简便,评价结果客观、合理,为湖泊水质的富营养化评价提供一种简便实用的评价方法.     

水质综合评价的基于熵权的灰色关联模型

水质评价需综合考虑各指标的属性，利用单指标评价常常具有独立性和不相容性，在将其视为灰色的基础上，由计算各样本的灰色关联度来确实其所属级别，引入信息熵理论，运用熵值法来确实评价指标的权重，建立了基于熵权和灰色关联度的水质综合评价模型，其计算简单，同时解决了权重计算困难的问题。通过在邯郸地下水水质评价中的应用，表明其结果合理，适用于水质评价。     

基于熵权的巢湖水生态健康模糊综合评价

在对巢湖水生态状况深入调查研究的基础上，确立了湖泊生态系统健康评价指标体系，确定了各指标健康等级的阙值范围，建立了基于熵权的湖库水生态健康模糊综合评价模型。并选取1980年巢湖的水质和水生态数据作为本底值。运用熵权法对2000～2005年整个湖泊生态系统的健康状况进行了客观评价，结果表明2004—2005年巢湖水生态系统均处于较差健康状态。本方法的计算结果表明采用熵权综合健康指数法对其进行生态系统健康评价具有很好的可靠性和实用性。最后根据实际提出了湖库水生态安全综合管理对策。     

传输法对不同方法制备频率选择表面的测试应用

随着频率选择表面(FSS)的广泛应用,精确的制备和测量技术验证理论分析日趋重要。本文采用丝网印刷法和激光加工法实现了计算中采用的频率选择表面周期单元的制备。基于Agilent E8257D信号发生器、Agilent E7405A频谱分析仪,采用传输法对制备的不同尺寸频率选择表面进行测试,得到了各种频率选择表面的频响特性。通过比较测试和仿真结果,验证了频率选择表面结构参数对频响特性的影响规律的正确性。同时,比较测试和仿真结果,得出激光加工法获得的频率选择表面精度更高的结论。     

地下水污染潜势评估方法及应用

本文阐述了地下水污染潜势评估的重要性，介绍了三种方法，对其中的废物－土址－地点相互作用列表法作了详细介绍，并将ＷＳＳＩＭ法用于四川省什邡县化工开发区地下不污染潜势估。     

基于熵值法的城市土地集约利用评价——以徐州市为例

从社会、经济和生态环境三个方面对城市土地集约利用的内涵进行了界定，构建了城市土地集约利用评价指标体系，利用熵值法确定指标权重，采用综合加权法计算城市土地利用的集约度。比徐州市为例进行了实证研究。并对评价结果进行分析，提出了针对性的建议。     

基于Wiener过程的霍尔电流传感器可靠性预测

霍尔电流传感器的退化机理复杂，退化具有波动性，非线性等特征，对高可靠、长寿命的霍尔电流传感器准确进行寿命预测是一个难点。本研究通过对霍尔电流传感器进行加速退化试验，利用性能退化数据，预测了传感器的可靠性及寿命。首先分析了霍尔电流传感器工作原理和退化机理，确定将输出电流漂移作为其性能退化参数。然后由试验数据推导得到Wiener过程漂移参数和扩散参数的约束关系，结合阿伦尼乌斯模型推导得到漂移参数和扩散参数的加速模型。从而得到霍尔电流传感器在正常工作温度条件下的可靠度函数和可靠寿命。将结果与基于加速退化轨迹法的可靠性预测结果进行对比，验证了本方法的可行性。     

大气环境容量计算方法A值法的修正探讨

大气环境容量常用的计算方法是A值法,A值法具有公式简单,参数少,便于计算等优点,但A值法未充分考虑当地气候、干湿沉降及污染物衰减等因素,因此,计算结果与给定区域内的实际大气环境容量有一定差距,因此,提出了修正A值法,修正A值法内增加了风速、大气混合层高度、污染物转化沉积等参数,促使计算结果比A值法更加科学。     

PARAMETER ESTIMATION FOR TPLN DISTRIBUTION FOR FLOOD FREQUENCY ANALYSIS1

ABSTRACT: The principle of maximum entropy (POME) was used to derive an alternative method for parameter estimation for the three parameter lognormal (TPLN) distribution. Six sets of annual peak discharge data were used to evaluate this method and compare it with the methods of moments and maximum likelihood estimation.     

DERIVATION OF THE GAMMA DISTRIBUTION BY USING THE PRINCIPLE OF MAXIMUM ENTROPY (POME)1

The principle of maximum entropy (POME) was used to derive the two-parameter gamma distribution used frequently in synthesis of instantaneous or finite-period unit hydrographs. The POME yielded the minimally prejudiced gamma distribution by maximizing the entropy subject to two appropriate constraints which were the mean of real values and the mean of the logarithms of real values of the variable. It provided a unique method for parameter estimation. Experimental data were used to compare this method with the methods of moments, cumulants, maximum likelihood estimation, and least squares.     

Simulating solute transport in a structured field soil: uncertainty in parameter identification and predictions

Dual-permeability models have been developed to account for the significant effects of macropore flow on contaminant transport, but their use is hampered by difficulties in estimating the additional parameters required. Therefore, our objective was to evaluate data requirements for parameter identification for predictive modeling with the dual-permeability model MACRO. Two different approaches were compared: sequential uncertainty fitting (SUFI) and generalized likelihood uncertainty estimation (GLUE). We investigated six parameters controlling macropore flow and pesticide sorption and degradation, applying MACRO to a comprehensive field data set of bromide andbentazone [3-isopropyl-1H-2,1,3-benzothiadiazin-4(3H)-one-2,2dioxide] transport in a structured soil. The GLUE analyses of parameter conditioning for different combinations of observations showed that both resident and flux concentrations were needed to obtain highly conditioned and unbiased parameters and that observations of tracer transport generally improved the conditioning of macropore flow parameters. The GLUE "behavioral" parameter sets covered wider parameter ranges than the SUFI posterior uncertainty domains. Nevertheless, estimation uncertainty ranges defined by the 5th and 95th percentiles were similar and many simulations randomly sampled from the SUFI posterior uncertainty domains had negative model efficiencies (minimum of -3.2). This is because parameter correlations are neglected in SUFI and the posterior uncertainty domains were not always determined correctly. For the same reasons, uncertainty ranges for predictions of bentazone losses through drainflow for good agricultural practice in southern Sweden were 27% larger for SUFI compared with GLUE. Although SUFI proved to be an efficient parameter estimation tool, GLUE seems better suited as a method of uncertainty estimation for predictions.     

EVALUATION OF METHODS FOR ESTIMATING STREAM WATER QUALITY PARAMETERS IN A TRANSIENT MODEL FROM STOCHASTIC DATA1

ABSTRACT: In this study the estimation of parameters in water quality models represented by linear first order partial differential equations is investigated. Two sets of simulated input-output data, one with input noise and the other with output measurement error, were used. The parameters were estimated by a gradient technique (Bard's method) and a pattern search technique. The results indicate that the output measurement error significantly affects the values of parameter estimates as compared to the noise added to the input. Bard's method consistently gave results with a smaller sum of square value.     

LINEAR PROGRAMS FOR NONLINEAR HYDROLOGIC ESTIMATION1

ABSTRACT: The minimization of the sum of absolute deviations and the minimization of the absolute maximum deviation (mini-max) were transformed into equivalent linear programs for the estimation of parameters in a transient and linear hydrologic system. It is demonstrated that these two methods yield viable parameter estimates that are globally optimal and reproduce properly the timing and magnitude of hydrologic events and associated variables such as total runoff. The two linear estimation methods compared favorably with the popular least-squares nonlinear estimation method. The generality of the theoretical developments shows that linear program equivalents are adequate competitors of nonlinear methods of hydrologic estimation and parameter calibration.     

Problems in Extinction Model Selection and Parameter Estimation

It is a vexing problem to achieve a consensus about the proper scientific way to assess population viability for habitat conservation plans. Rather than a hypothesis-testing approach, here it is proposed to select population models, estimate extinction parameters, and assess prediction uncertainty using a pragmatic, empirical Bayesian approach. The simplest usable models include the effects of population growth, r; carrying capacity, K; Allee threshold, N(A); and environmental stochasticity, v(r). Analytic predictions of expected extinction times are available for such models. Models that are more complex can be elaborated from this basis. Selection from a hierarchy of nesting population models can often be done through the evaluation of parameters. The estimation of the most important extinction parameters can be undertaken in a variety of ways. Time series can be analyzed to estimate r(d), v(r), rho, and K. Habitat models and individualistic population models may help estimate N(A) and K and demographic stochasticity. Fine-scale biogeography and climatological data may be useful in the estimation of a variety of parameters. Because it takes many years to estimate extinction parameters accurately for a given population of interest, the most efficient estimation procedures are desirable. I propose the use of prior information from an (as yet nonexistent) population biology database. The accumulation of local information through monitoring will improve our estimates allowing adaptive management. Uncertainty in the estimates will always remain, but it may be quantified by the posterior distributions. A crude example is discussed using treefrog population data. Although the motivations, beliefs, and biases of competing stakeholders will differ, a habitat conservation plan could accommodate this variation in the prior distributions. Field experience from monitoring will increasingly clear up any discrepancies between the opposing beliefs and the real ecosystem. As the world is an uncertain place and because there is no universal scientific method, there will always be controversy and surprises. The best we can do is (1) agree about our prior information, (2) agree about the strategy of model selection and parameter estimation, and (3) agree about our strategy for adaptive management. Perhaps the greatest impediment to such prior agreements for HCPs is the likely paranoia inspired by the use of unfamiliar statistical methodology. We need to train students of ecology in a more flexible and deeper understanding of statistics and philosophy of science.     

REGIONALIZATION OF LOW-FLOW FREQUENCY ESTIMATES: AN ALABAMA CASE STUI)Y1

ABSTRACT: Low-flow estimates, as determined by probabilistic modeling of observed data sequences, are commonly used to describe certain streamflow characteristics. Unfortunately, however, reliable low-flow estimates can be difficult to come by, particularly for gaging sites with short record lengths. The shortness of records leads to uncertainties not only in the selection of a distribution for modeling purposes but also in the estimates of the parameters of a chosen model. In flood frequency analysis, the common approach to mitigation of some of these problems is through the regionalization of frequency behavior. The same general approach is applied here to the case of low-flow estimation, with the general intent of not only improving low-flow estimates but also illustrating the gains that might be attained in so doing. Data used for this study is that which has been systematically observed at 128 streamflow gaging sites across the State of Alabama. Our conclusions are that the log Pearson Type 3 distribution is a suitable candidate for modeling of Alabama low-flows, and that the shape parameter of that distribution can be estimated on a regional basis. Low-flow estimates based on the regional estimator are compared with estimates based on the use of only at-site estimation techniques.     

REGIONALIZATION AND PREDICTION OF FLOODS IN THE FRASER RIVER CATCHMENT,B.C.1

Probability distributions that model the return periods of flood characteristics derived from partial duration series are proposed and tested in the Fraser River catchment of British Columbia. Theoretical distributions describing the magnitude, duration, frequency and timing of floods are found to provide a goof fit to the observed data. The five estimated parameters summarizing the flood characteristics of each basin are entered into a discriminant analysis procedure to establish flood regions. Three regions were identified, each displaying flood behavior closely related to the physical conditions of the catchment. Within each region, regression equations are obtained between parameter values and basin climatic and physiographic variables. These equations provide a satisfactory prediction of flood parameters and this allows the estimation of a comprehensive set of flood characteristics for areas with sparse hydrologic information.