Video Prediction Based on Multi-Resolution Echo State Networks for a Jar Test

For a jar test in water purification, it is necessary to develop a video prediction method to assist in coagulant dosage decisions. However, such a method has not been established; video prediction methods in other fields require a large amount of training videos. This paper proposes a video prediction method based on multi-resolution echo state networks (ESN) for a jar test. Because the proposed method is constructed based on ESN, we succeed in long-term video prediction even if the amount of training videos is small. Specifically, we develop a new model that performs ESN while observing the input video at different resolutions and effectively fuses the prediction results. The multi-resolution approach enables us to acquire both rough shape changes and fine pattern features, resulting in accurate prediction. In fusion of the multiple prediction results, our method can calculate optimal weights by the least squares method. Experimental results for a jar test confirm that our method outperformed existing video prediction methods based on deep neural networks. In addition, the results on the sky timelapse dataset show the versatility of our method.