Optimum discrete linear hydrologic systems with multiple inputs by Juan Camilo Ochoa Restrepo

Cover of: Optimum discrete linear hydrologic systems with multiple inputs | Juan Camilo Ochoa Restrepo

Published by Dept. of Civil Engineering, Massachusetts Institute of Technology in Cambridge, Mass .

Written in English

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Subjects:

  • Hydrologic models.,
  • Hydrology -- Mathematical models.

Edition Notes

Book details

Statementby Juan Camilo Ochoa Restrepo and Peter S. Eagleson.
SeriesMassachusetts Institute of Technology. Hydrodynamics Laboratory. Report -- no. 80., Massachusetts Institute of Technology. Dept. of Civil Engineering. Research report -- R65-22., R (Massachusetts Institute of Technology. Dept. of Civil Engineering) -- 65-22.
ContributionsEagleson, Peter S.
The Physical Object
Paginationviii, 96 p. :
Number of Pages96
ID Numbers
Open LibraryOL14176974M

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LECTURE 1: HYDROLOGIC SYSTEMS 3 The Systems Approach 3 Linear Time-Invariant Systems 17 Identification and Simulation. 29 Problems on Hydrologie Systems 39 Literature Cited 40 LECTURE 2: REVIEW OF PHYSICAL HYDROLOGY 43 Precipitation 44 Evaporation and Transpiration 45 Infiltration and Percolation The Wiener‐Hopf theory of optimum linear systems is applied to the determination of the stable pulse response of a monotone hydrologic system from coincident records of input and output in the form of discrete time series.

In application to the rainfall runoff system, linear programming methods are used in the solution of the Wiener‐Hopf Cited by: precisely represent natural systems • There is no single, accepted statistic or test that determines whether or not a model is valid • Both graphical comparisons and statistical tests are required in model calibration and validation • Models cannot be expected to be more accurate than the errors (confidence intervals) in the input and.

Lecture: Discrete-time linear systems Discrete-time linear systems Discrete-time linear system 8 input sequence u(k), k 2N, it is possible to predict the entire sequence of states x(k) and outputs y(k), 8k 2N The state x(0) summarizes all the past history Optimum discrete linear hydrologic systems with multiple inputs book the system The dimension n of the state x(k.

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The present study demonstrates the capability of two preprocessing techniques such as wavelets and moving average (MA) methods in combination with feed-forward neural networks—namely, back propagation (BP) and radial basis (RB) and multiple linear regression (MLR) models—in the prediction of the daily inflow values of the Malaprabha reservoir in Belgaum, India.

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Modeling complex systems. Pairs of discrete random variables. The joint cdf of X and Y. Response of a linear system to random input. Time averages of random processes and ergodic theorems. Fourier series and Karhunen-Loeve expansion. Karhunen-Loeve expansion.

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Time averages of random processes and ergodic theorems. Fourier series and Karhunen-Loeve expansion. Karhunen-Loeve expansion. Summary. Problems. Analysis and Processing of Random Signals. Power spectral density. Continuous-time random processes. Discrete-time random processes.

where Y is a vector of model outputs, P() denotes the nonlinear hydrologic model, is a matrix of input values (e.g., precipitation and evapotranspiration), θ is a vector of model parameters, and e is a vector of mutually independent and normally distributed errors with zero mean and constant variance.

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The objectives of this course are to provide the participant with the background required for. On the basis of it, a new RTD-A controller for Multiple Input Multiple Output system (MIMO) is illustrated and the global optimal control solution is also discussed in this paper.

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This expression simplifies the calculation of the optimum .Daily data on 11 years of rainfall, inflow, and streamflow at an upstream gauging station have been used.

The observed inputs are decomposed into subseries using discrete wavelet transform with different mother wavelet functions, and then the appropriate subseries is used as input to the neural networks for forecasting reservoir inflow.

The inputs, x i for i = 1,p, of the Wiener network are the measured noninvasive variables or disturbances (i.e., food, activity, and stress) and the output, y, is input has its own linear dynamic block, G i, and each dynamic block has an intermediate unobservable, output v i, which represents the independent dynamic response of its corresponding input.

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