Sorry, you need to enable JavaScript to visit this website.

RIVER FLOW PATH CONTROL WITH REINFORCEMENT LEARNING

Primary tabs

Citation Author(s):
Dongqi LIU, Yutaka NAITO, Chen ZHANG, Shogo MURAMATSU,Hiroyasu YASUDA, Kiyoshi HAYASAKA, Yu OTAKE
Submitted by:
Dongqi Liu
Last updated:
23 August 2021 - 1:45pm
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Dongqi Liu
Paper Code:
ICAS 2021-100

Abstract 

Abstract: 

In this study, a cyber-physical system (CPS) for river flow path control is proposed using reinforcement learning. Recently, there has been a frequent occurrence of river flooding due to heavy rains, resulting in serious economic losses and victims. One cause of river flooding is the meandering due to the river bed growing and flow path change. As a mean of avoiding the meandering, river groynes can be used to regularize the flow. However, the mechanism of the flow path growing, and its optimal control is unclear. Therefore, in this study, a dynamic flow path control system is proposed using a data-driven approach to solve the problem at once. As a data-driven approach, reinforcement learning is adopted. The proposed system is designed to control meandering by adaptively deforming and moving the groynes with the reward of the flow path health. The effectiveness of the proposed flow path control system is verified through a simulation of the river model.

up
0 users have voted:

Dataset Files

ICAS2021_final.pdf

(15)