INTELLIGENT PREDICTION OF URBAN WATER QUALITY THROUGH MULTI-SOURCE DATA INTEGRATION

Abstract

Maintaining optimal urban water quality is a critical component of sustainable city management and public health protection. Traditional water monitoring systems rely on manual sampling and isolated sensor networks, which often fail to capture the dynamic nature of environmental and anthropogenic factors influencing water quality. To address these challenges, this study proposes an intelligent, data-driven framework for predicting urban water quality through the integration of multisource ubiquitous data. The framework combines real-time environmental sensing, meteorological information, land-use data, and urban infrastructure parameters to create a holistic understanding of water systems. Machine learning and deep learning models, including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks, are employed to capture both spatial and temporal dependencies in complex datasets. Feature selection and correlation analysis are used to identify the most influential factors affecting key water quality indicators such as pH, dissolved oxygen, turbidity, and chemical oxygen demand. The proposed approach demonstrates that integrating heterogeneous data sources significantly enhances predictive performance and enables timely identification of potential pollution events. Experimental validation on multi-city datasets reveals that the integrated model outperforms traditional single-source prediction methods in terms of accuracy, precision, and robustness. The framework not only supports real-time decision-making for environmental agencies but also contributes to developing smart water management systems in modern urban environments.