The paper presents a new approach to solving the problem of water quality control in rivers. We proposed an intelligent system that monitors and controls the quality of water in a river. The distributed measuring system works with a central control system that uses the intelligent analytical computing system. The Biochemical Oxygen Demand (BOD) and Dissolved Oxygens (DO) index was used to assess the state of water quality. Because the results for the DO measurement are immediate, while the measurement of the BOD parameter is performed in a laboratory environment over a period of several days, we used Artificial Neural Networks (ANN) for immediate estimation BOD to overcome the problem of controlling river water quality in real time. Mathematical models of varying complexity that represent indicators of water quality in the form of BOD and DO were presented and described with ordinary and distributed-parameters differential equations. The two-layered feed-forward neural network learned with supervised strategy has been tasked with estimating the BOD state coordinate. Using classic ANN properties, the difficult-to-measure river ecological state parameters interpolation effect was achieved. The quality of the estimation obtained in this way was compared to the quality of the estimation obtained using the Kalman–Bucy filter. Based on the results of simulation studies obtained, it was proved that it is possible to control river aeration based on the measurements of particular state coordinates and the use of an intelligent module that completes the “knowledge” concerning unmeasured data. The presented models can be further applied to describe other cascade objects.
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