WQ-ERT: Water Quality Prediction in Low-Resource Domains Using Gaussian Noise-Augmented Transformer Encoder: Case Study in Dili, Timor-Leste

Evaluating water quality is fundamental to ensuring that freshwater ecosystems support the well-being of all life on Earth. The present study uses physicochemical parameters to assess groundwater in Dili, Timor-Leste, from 2011 to 2018. Water quality was determined using the conventional Water Quality Index (WQI), aggregated via a weighted sum function aligned with the World Health Organization (WHO). The study aims to predict the WQI and water quality status, expressed as Water Quality Classification (WQC), to support sustainable water resource management. In low-resource domains, it is crucial to address challenges related to small dataset size and class imbalance in the sample observations. However, deep learning models are highly dependent on the availability and quality of training data. This paper builds upon prior data augmentation strategies for water quality data analysis, aiming to overcome the constraints of a small dataset in predicting the WQI and WQC. To address these issues, the study extends prior data augmentation strategies and introduces WQ-ERT (Water Quality Encoder Representations from Transformer). This Transformer-based model applies Gaussian noise augmentation and a class-balancing strategy. The model has been pre-trained on a large Indian water quality dataset with similar soil-type characteristics and fine-tuned on the Timor-Leste water quality dataset, using a Gaussian noise value of α = 0.20 for the Indian datasets, whereas for Timor-Leste, it uses a lower Gaussian noise value of α = 0.05. Furthermore, to address label imbalance in the classification target, a random search-based class-balancing technique is employed during fine-tuning. We conducted six experiments to evaluate our proposed model and applied five-fold cross-validation. The results demonstrate that noise augmentation substantially improves WQC performance, achieving 97.0% balanced accuracy, and WQI prediction reaches an R2 of 0.703, indicating better generalization across the dataset. Class-balancing techniques enhance WQC, particularly in balanced accuracy, F1-score, and the Matthews Correlation Coefficient (MCC). This configuration reduces false negatives by 86.05% for water quality classification, though excessive weighting slightly lowers precision. In WQI prediction, the experiment shows a 7.92% reduction in MAE, improving few-shot adaptation to Timor-Leste without distortion.