AN ADAPTIVE IDENTIFICATION AND PREDICTION ALGORITHM FOR THE REAL-TIME FORECASTING OF HYDROLOGICAL TIME SERIES / Un algorithme qui se montre adaptable aux exigences de l'identification et de la prévision en ce qui concerne la prévision en temps réel des séries hydrologiques

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Abstract
In order to achieve the effective control of water resources systems, one must know the future behaviour of the inputs to that particular system. Because of the uncertainties inherent in water resources processes the prediction algorithm to be constructed should include stochastic elements too. Moreover, the algorithm should be recursive to avoid cumbersome computations and be capable of use in real-time forecasting. A method is presented which is applicable for both linear and nonlinear hydrological systems not having completely time invariant properties. The algorithms are based on the state-space description of the processes involved and utilize the Kalman stochastic filtering technique. Due to the unknown nature of noise processes, the basic algorithms were changed to be adaptive. Using the algorithms the joint handling of water quantity and quality data becomes feasible.

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