Applying adaptive prediction to sea-water quality measurements

This study explores the possibility of using adaptive filters to predict sea-water quality indicators such as water temperature, pH and dissolved oxygen based on measurements produced by an under-water measurement set-up. Two alternative adaptive approaches are tested, namely a projection algorithm and a least squares algorithm. These algorithms were chosen for comparison because they are widely used prediction algorithms. The results indicate that if the measurements remain reasonably stationary, it is possible to make one-day ahead predictions, which perform better than the prediction that the value of a certain quality variable tomorrow is going to be equal to the value today.

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