Work

Machine Learning for Surface Water Quality Management in Malaysia: Integrating Physicochemical Parameters, Nutrient Loads, Land Use, Hydrological Conditions, and Anthropogenic Pressure

Year: 2026

Type: article

Abstract: Surface water quality in Malaysia is increasingly influenced by rapid urbanization, industrial expansion, agricultural activities, wastewater discharge, climatic variability, and changes in hydrological regimes. These interacting pressures create complex and nonlinear relationships among physicochemical water-quality parameters, watershed characteristics, and pollutant dynamics, making conventional monitoring approaches insufficient for comprehensive and timely assessment. This study develops a data-driven machine learning framework for surface water quality assessment, prediction, pollution-event detection, and management in Malaysian river systems. The proposed framework integrates physicochemical, hydrological, spatial, land-use, and anthropogenic variables to characterize the temporal and spatial variability of water-quality conditions. The investigated parameters include dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, pH, temperature, electrical conductivity, turbidity, total suspended solids, ammoniacal nitrogen, nitrate, phosphate, total nitrogen, and total phosphorus, together with rainfall, streamflow, water level, antecedent rainfall, urbanization, agricultural land use, industrial land use, population density, wastewater pressure, and road density. Seven machine learning approaches are incorporated, including Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), Support Vector Regression (SVR), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and hybrid ensemble models. The Malaysian National Water Quality Standards and Water Quality Index (WQI) are incorporated to establish the regulatory and quantitative basis for water-quality classification and prediction. The framework further integrates explainable artificial intelligence techniques, including SHAP and LIME, to quantify predictor contributions and identify the dominant environmental and anthropogenic factors governing model outputs. The proposed methodology enables simultaneous water-quality classification, continuous prediction, anomaly and pollution-event detection, source-pressure identification, and management-oriented decision support. Particular emphasis is placed on temporal variability, nonlinear interactions, hydrological influence, land-use effects, and the integration of multiple environmental data sources. The study provides a comprehensive Malaysia-oriented machine learning framework that can enhance conventional surface-water monitoring by combining predictive modeling, explainability, and integrated watershed information. The findings establish a methodological basis for developing real-time and transferable water-quality prediction systems capable of supporting pollution mitigation, watershed management, and sustainable water-resource planning across Malaysian river basins.

Source: The International Journal of Engineering Sciences

DOI: 10.64440/ijes/engineeringx0021