U.S. EPA Act of 2000 requires states with beaches to cooperate with the EPA to monitor the level of bacteria that impact water quality and issue swimming advisories when threshold levels are violated. In Texas, this monitoring system is operated by the Texas Beach Watch Program (TBWP) under the Texas General Land Office (TGLO). Two main methods are currently used to assess water quality: culture-based assays and rapid detection methods (quantitative polymerase chain reaction or qPCR) for Enterococcus. The former method can take hours to yield results. For users of the coastal waters, the delay can mean the information is obsolete when reports are publicized. Results can be obtained in less than six hours for qPCR, but the analysis is costly and requires qPCR expertise, which is not readily available.
The goal of the project "AI-enabled Enterococcus Predictor for Texas Coastal Ocean Beach: ePredictor" is to support the Texas Coastal Management Program (CMP) through the development of a prototype of an Enterococci prediction system (hereafter, e-Predictor) that can be scaled to all sampling areas in Texas, to accelerate both the quantification of coastal ocean bacteria and the dissemination of results to relevant agencies and the public. Artificial Intelligence (AI) and Machine Learning (ML) algorithms are employed to develop the models. The results will be made public in later phases of development via TGLO sites.
The Enterococcus Predictor, an AI-enabled system to predict the level or counts of Enterococcus bacteria for a geographical area, is currently in development. If you are a registered user, please use your Google account to log in. If you are having access issues, please contact felimon.gayanilo@tamucc.edu.