Stream Temperature Estimation Using Kalman Filter

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Abstract
The system model used combines the deterministic sinusoidal model with a simple stochastic (first-order autoregressive) model of the residuals from the actual temperature of the temperature estimation given by the sinusoidal model. The estimation and forecasting technique was tested for its capability to generate daily streamflow temperature between several different measurement intervals. Results with Kalman filter are compared with those obtained in the four parallel models (the sinusoidal, the sinusoidal coupled with either the first-order or second-order autoregressive modeling of the random deviations, and the sinusoidal coupled with first-order autoregressive-moving average modeling of the random deviations). The mean-square deviation of estimated or predicted temperatures from the actually observed is used to measure the relative accuracy of the estimations by the five different techniques. The results show a definite advantage of using Kalman filter.

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