Predicting Surface Water Bacteria Levels Using Transfer Learning and Domain Adaptation

Surface water contaminated by fecal bacteria can cause diarrheal illness, threatening human's health (especially among children). In recent years, supervised machine learning (ML) has been used to predict fecal indicator bacteria (FIB) levels. However, training ML models is challenging and, in some cases, even impractical due to sparsity of labeled data in all locations (e.g., in rural areas or low-income countries). In this paper, we introduce the largest water quality dataset available collected from beaches in Chicago and San Diego, USA. We utilized various models to predict historical FIB levels on this dataset establishing strong baseline models for supervised learning and transfer learning. Our models include Random Forest (RF), extreme gradient boosting (XGBoost), and attentionbased tabular deep learning (TabNet) models. Additionally, given the widespread use of large language models (LLMs), we have fine-tuned the LLaMA3-8B model for regression in a tabular-to-text setting. Our results show that supervised and unsupervised domain adaptation methods can enhance transfer learning performance. Specifically, the supervised methods, especially RF, represent a promising solution for FIB level prediction, while domain adaptation could be successfully employed to predict FIB levels in locations where they are rarely measured. Our code and dataset are available on: https://github.com/aliielahi/ONR-WQ.