Evaluation of PINN considering the confluence information on open channels

Abstract

Deep learning models incorporating physical information (PINN) have been attracting attention in recent years. In general, PINN models can perform good reproduction calculations even in the areas where there are no observed values using physical laws and nearby observed values. However, to apply PINN to the flows of drainage channels at agricultural irrigation facilities, it is necessary to consider the confluence of channels. Therefore, our PINN (GPINN) contained a graph embedding function that can represent the confluence information of cannels to calculate the flows at the confluence of canals. Our PINN provided poor and good reproducibility results (water depth and flow velocity) depending on the event when simulating multiple artificially generated flood events. The best GPINN result of the flow reproducibility greatly reproduced the changes in water depth at the confluence on two canals.