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  • Articles  (3)
  • Kalman filtering  (3)
  • 1985-1989  (3)
  • 1970-1974
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  • 1945-1949
  • Geography  (3)
  • 1
    Electronic Resource
    Electronic Resource
    Basinwide flood control system by combining prediction and reservoir operation (1989)
    Springer
    Stochastic environmental research and risk assessment 3 (1989), S. 31-49 
    Details
    ISSN: 1436-3259
    Keywords: Reservoir operation ; prediction ; Kalman filtering ; flood prevention ; fuzzy control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Japan has traditionally performed flood prevention through the construction and use of dikes, storage reservoirs, and basins which are costly and time consuming options. Another non-structural option is to operate the flood control system appropriately with a view to reducing flood damage. In this paper, a flood control system combining the runoff prediction model in the whole river basin with the reservoir operation is discussed. Different models of the runoff process are introduced in order to compare their accuracies and the computational time for the flood forecasting system. The reservoir operational rule is formulated in terms of fuzzy inference theory. Historical data are applied in a case study for verification of the proposed theories.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01543426
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Comparative analysis of three recursive real-time river flow forecasting models: deterministic, stochastic, and coupled deterministic-stochastic (1988)
    Springer
    Stochastic environmental research and risk assessment 2 (1988), S. 17-33 
    Details
    ISSN: 1436-3259
    Keywords: River flow forecasting ; discrete linear cascade model ; ARMAX ; coupled models ; Kalman filtering ; Danube
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract The objective of the paper is to compare three recursive linear state space models used to forecast river flow. The three models are as follows: (i) Purely deterministic discrete linear cascade model (DLCM); (ii) Purely stochastic autoregressive moving average (ARMAX) time series model; and (iii) Coupled deterministic (DLCM) — stochastic (ARMA) model. Description of DLCM is given shortly. The state space formulation of the ARMAX model enables the recursive estimation of random walk parameters and the forecast of flows by linear Kalman filtering. The correlated error sequence of DLCM is described by an ARMA model. The DLCM and ARMA models are put together in a coupled deterministic-stochastic model. The recursive conditional forecasting of the augmented state vector is performed by the linear Kalman-filter. The conditional output forecast is given by linear projection of thea priori state vector. Numerical investigations on River Danube data lead to the conclusion that the coupled deterministic-stochastic model is the most efficient forecasting model of all the three recursive techniques compared.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01544191
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  • 3
    Electronic Resource
    Electronic Resource
    Practical aspects of stochastic dynamic tidal modelling (1988)
    Springer
    Stochastic environmental research and risk assessment 2 (1988), S. 137-150 
    Details
    ISSN: 1436-3259
    Keywords: shallow water equations ; Kalman filtering ; data assimilation ; observability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Kalman filtering for stochastic dynamic tidal models, is a hyperbolic filtering problem. The questions of observability and stability of the filter as well as the effects of the finite difference approximation on the filter performance are studied. The degradation of the performance of the filter, in case an erroneous filter model is used, is investigated. In this paper we discuss these various practical aspects of the application of Kalman filtering for tidal flow identification problems. Filters are derived on the basis of the linear shallow water equations. Analytical methods are used to study the performance of the filters under a variety of circumstances.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01543456
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