FLOW-PATH FITTING FROM IMAGES WITH FOURIER BASIS FOR RIVER HEALTH ASSESSMENT

This study proposes a flow-path fitting method to asses river health condition. In recent years, river flooding due to abnormal weather has been a growing problem in many parts of the world. Meandering of rivers is one of the causes of river flooding. In order to solve this problem, the authors have proposed a river flow path control cyber-physical system (CPS). The CPS adopts reinforcement learning (RL) to control actuators that act as groyens. To realize the RL, a reward is needed to index the river health. First, this paper defines a river health index on the assumption that the flow path is represented by a function, and evaluates its energy. However, it is not trivial to identify the dominant path from river videos captured by cameras or radars due to false detections, undetections and noise. In order to obtain a dominant path by image processing, this study reduces the problem to a group LASSO one using Fourier basis for unequally spaced and repeatedly sampled noisy data. The solver is given by ADMM. The significance of the proposed method is verified by evaluating its performance through simulations using artificial data and experiments using a river model setup.