Variational Algorithms for Identification of Transfer Model Parameters and Experimental Planning

The aim of the work is to build and implement variational algorithms for identifying the capacities of a series of pollution sources in a passive impurity transfer model. The paper considers an example of variational assimilation of impurity concentration data in the upper layer based on minimizing the prediction quality functional and an approach based on variational filtering of linear systems of equations. In both cases, an essential element of the algorithms is the solution of related problems, which are also used in the construction of information matrices. The paper considers an example of variational assimilation of data on the concentration of suspended matter in the upper layer of the Sea of Azov in the area of the Dolgaya Spit, which is of great importance for navigation in this area. When solving the problem of identifying the power of pollution sources in the passive impurity transfer model based on measurement data, the question arises of building optimal plans to improve the computational properties of algorithms. The computational properties of the algorithms used in this case can be significantly improved by choosing the most optimal measurement scheme.