Spatiotemporal Water Quality Modeling Using Deep Learning Architectures

Abstract

Water quality monitoring is vital for ensuring public health, environmental sustainability, and economic resilience. Traditional monitoring techniques, while precise, often fall short due to high costs, labor intensity, and limited temporal and spatial resolution barriers that are increasingly critical amid accelerating urbanization, climate change, and pollution. This study explores the application of deep learning architectures to spatiotemporal water quality modeling, leveraging diverse datasets comprising historical records, sensor readings, and government sources. Supervised learning techniques were evaluated for predictive and classification tasks, including Support Vector Regression (SVR), Random Forests, XGBoost, and Decision Trees. SVR yielded strong regression performance for Water Quality Index (WQI) prediction with an R² of 0.9693 and low mean squared error, while XGBoost and Decision Trees demonstrated robust classification accuracy above 94%, with Decision Trees excelling in macro-averaged metrics. Unsupervised learning using DBSCAN revealed moderate clustering potential, but also emphasized the limitations of density-based approaches for noisy environmental data. Exploratory analyses offered insights into parameter distributions and interdependencies, including Kernel Density Estimation, correlation heatmaps, box plots, PCA, and t-SNE. While the study confirms the potential of AI in water quality monitoring, it also underscores challenges such as data imbalance, limited minority class precision, and the need for interpretable and scalable models. Future work should integrate explainable AI, edge computing, and hybrid domain-informed models to foster real-time, equitable, and sustainable water monitoring solutions aligned with SDG 6. This research demonstrates the promise of deep learning in transitioning water quality management from reactive to predictive paradigms.