Identification in nonlinear, distributed parameter water quality models

Abstract

Systematic and efficient numerical algorithms are developed and applied to the identification of unknown functional parameters in nonlinear estuarine water quality models based on input‐output measurements. As an illustration of the methodology the longitudinal dispersion coefficient is identified from an intratidal, time‐varying, variable area, salinity intrusion model by using both simulated data and actual data from the Delaware River estuary. A comparison among three proposed algorithms through extensive simulation research shows that Marquardt's algorithm emerged as the most efficient one. Effects of noise content and the number of data measurement locations on parameter sensitivity are investigated. Actual monitored salinity data for 3 days in September 1965 are tested in the saline portion of the Delaware River estuary. The spatial variation of the longitudinal dispersion coefficient for this period is estimated. The results obtained indicate that the methodology is generally applicable and it represents a different and supplementary alternative to the methods based on analytical predictions and empirical correlations.