SGA-WQI: SHAP-Guided Adaptive Water Quality Index for Deep Learning-Based Water Quality Prediction Using TabNet and Transformer Models

Clean and safe water is essential for human health,
environmental sustainability, and economic development. However, water quality is rapidly degraded by industrial, agricultural,
and urban pollution. Traditional monitoring relies on manual
laboratory sampling, which is costly, time-consuming, and unsuitable for real-time analysis. To solve these limitations, this study
proposes an explainable deep learning framework for multiclass water quality classification using the Water Quality Index (WQI). Three tabular deep learning architectures—TabNet,
TabTransformer, and FT-Transformer—were evaluated. For data
preprocessing, median-based missing value imputation, normalization, and SMOTE-based class balancing were included. Model
evaluation was conducted using accuracy, precision, recall, and
F1-score, with the help of SHAP-based explainability. SHAPbased explainability helped to analyze feature contributions.
Among all models, TabNet achieved the best performance with
an accuracy of 98.3 %. This paper also introduces a novel SHAPguided Adaptive Water Quality Index (SGA-WQI) that dynamically adjusts parameter weights and captures feature interactions
which improves model interpretability and robustness for realworld environmental monitoring.