Video Prediction Based on Multiresolution Echo State Networks to Support Coagulant Dosage Decisions

Water purification treatments often use the jar test to determine the appropriate coagulant dosage. In the jar test, it is necessary to develop a method for predicting future video frames after dosing a water sample with the coagulant. For this purpose, the present paper describes a multiresolution echo state network (ESN) with two novel features. This study is the first to realize video prediction for the jar test in water purification. The first novelty of the proposed method is long-term video prediction from a small number of training videos. This overcomes the need for many training videos when applying video prediction methods developed in other fields to our task. The second novelty is a multiresolution analysis framework for ESN-based video prediction. This solves the problem of existing recursive prediction strategies accumulating errors over time, eventually producing trivial results. The strength of our proposed method is that prediction results at multiple resolutions effectively remove errors at specific resolutions. Experiments using kaolin and zeolite show that the proposed method achieves improved accuracy compared with state-of-the-art deep learning-based video prediction methods. Furthermore, compared with conventional methods, our method provides a better quantification of the visual information contained within the jar test results after dosing water with the coagulant.