A calibration framework toward model generalization for bacteria concentration estimation in water resource recovery facilities.

ABSTRACT: Reduced bacteria concentrations in wastewater is a key indicator of the efficacy of water resource recovery facilities (WRRFs). However, monitoring the presence of bacterial concentrations in real time at each stage of the WRRF is challenging as it requires taking and processing water samples offline. Although few studies have been proposed to predict bacterial concentrations using data-driven models, generalizing these models to unseen data from different WRRFs remains challenging. This paper proposes a calibration approach based on neural networks to adapt the optimal models across various WRRFs in Saudi Arabia for bacterial estimation at the influent and effluent stages. The calibration relies on the out-of-distribution (OOD) framework of the physiochemical water parameters (e.g., pH, C

SUBMITTER: Aljehani F 

PROVIDER: S-EPMC11682458 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature