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  • Articles  (3)
  • parameter estimation  (3)
  • 1985-1989  (3)
  • 1950-1954
  • Geography  (3)
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Stochastic environmental research and risk assessment 3 (1989), S. 135-153 
    ISSN: 1436-3259
    Keywords: Stochastic partial differential equations ; maximum likelihood estimation ; parameter estimation ; moment equations ; stodhastic contaminant transport
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Many problems in hydraulics and hydrology are described by linear, time dependent partial differential equations, linearity being, of course, an assumption based on necessity. Solutions to such equations have been obtained in the past based purely on deterministic consideration. The derivation of such a solution requires that the initial conditions, the boundary conditions, and the parameters contained within the equations be stipulated in exact terms. It is obvious that the solution so derived is a function of these specified, values. There are at least four ways in which randomness enters the problem. i) the random initial value problem; ii) the random boundary value problem; iii) the random forcing problem when the non-homogeneous part becomes random and iv) the random parameter problem. Such randomness is inherent in the environment surrounding the system, the environment being endowed with a large number of degrees of freedom. This paper considers the problem of groundwater flow in a phreatic aquifer fed by rainfall. The goveming equations are linear second order partial differential equations. Explicit form solutions to this randomly forced equation have been derived in well defined regular boundaries. The paper also provides a derivation of low order moment equations. It contains a discussion on the parameter estimation problem for stochastic partial differential equations.
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Stochastic environmental research and risk assessment 1 (1987), S. 199-208 
    ISSN: 1436-3259
    Keywords: Extreme value distribution ; two-component distribution ; maximum entropy principle ; parameter estimation ; regional estimation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract The two component extreme value (TCEV) distribution has recently been shown to account for most of the characteristics of the real flood experience. A new method of parameter estimation for this distribution is derived using the principle of maximum entropy (POME). This method of parameter estimation is suitable for application in both the site-specific and regional cases and appears simpler than the maximum likelihood estimation method. Statistical properties of the regionalized estimation were evaluated using a Monte Carlo approach and compared with those of the maximum likelihood regional estimators.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1573-1650
    Keywords: Daily flows ; forecasting ; instrumental variable method ; parameter estimation ; rainfall filter
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Notes: Abstract The instrumental variable-approximate maximum likelihood (IV0AML) method provides a technique to develop better models for short-time increment hydrologic data. In this method, a recursive input-output model, which consists of a deterministic model and a stochastic noise model are used. These models handle the system and measurement noise separately. The instrumental variable method has been developed to eliminate the bias in parameter estimates. The IV-AML method is investigated in the present study. Parameters of daily rainfall-runoff models are estimated by the IV-AML and by least squares methods and compared. The effects of a rainfall filter on parameter estimates are also investigated. Forecast accuracies of models whose parameters are estimated by IV-AML and least squares methods are compared. The results indicate that the forecast accuracy of models whose parameters are estimated by least squares method is comparable to that of models whose parameters are estimated by IV-AML method. The rainfall filter, on the other hand, reduces the parameter variation and improves forecasts.
    Type of Medium: Electronic Resource
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