Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring.

ABSTRACT: To better control eutrophication, reliable and accurate information on phosphorus and nitrogen loading is desired. However, the high-frequency monitoring of these variables is economically impractical. This necessitates using virtual sensing to predict them by utilizing easily measurable variables as inputs. While the predictive performance of these data-driven, virtual-sensor models depends on the use of adequate training samples (in quality and quantity), the procurement and operational cost of nitrogen and phosphorus sensors make it impractical to acquire sufficient samples. For this reason, the variational autoencoder, which is one of the most prominent methods in generative models, was utilized in the present work for generating synthetic data. The generation capacity of the model was

SUBMITTER: Paepae T 

PROVIDER: S-EPMC9920320 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature