What question did this study set out to answer?

The aim is to use AI and machine learning to predict seasonal variations in water quality across major rivers.

February 11, 2026Open Access

Predicting Seasonal Variations in River Water Quality: An Artificial Intelligence (AI) Approach Integrating Physicochemical Parameters

Key Points

The aim is to use AI and machine learning to predict seasonal variations in water quality across major rivers.
Developed predictive models using machine learning algorithms like XGBoost, Random Forest, and Decision Tree.
Collected 476 water samples monthly from three rivers over a two-year period.
Applied K-fold cross-validation and hyperparameter optimization to enhance model accuracy.
Used Partial Dependence Plots to analyze the effect of water quality parameters on predictions.
Random Forest achieved the highest prediction accuracy of 79%.
XGBoost performed with an accuracy of 77%.
The models effectively identified conditions of deteriorated water quality.
Key drivers of seasonal variations included total dissolved solids, alkalinity, and electrical conductivity.

Abstract

The characterization and prediction of seasonal variations in river water quality are essential for maintaining control of aquatic ecosystems and resource management. This study aims to develop predictive models using Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) algorithms, to classify seasonal patterns in three major rivers in Bangladesh: Buriganga, Shitalakhya, and Turag. This study considered 15 of the most significant water quality parameters, including pH, alkalinity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDSs), and electrical conductivity (EC). A total of 476 samples were gathered on a monthly basis at 17 monitoring points in the three rivers, covering all months between January and December from 2021 to 2023. With K-fold cross-validation and hyperparameter optimization, three ML models, like Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision Tree (DT), were employed for predicting seasonal variation in river water quality. The models were assessed based on accuracy, precision, recall, F1, and ROC–AUC scores. Partial Dependence Plot (PDP) analysis was applied to explore the marginal effects of key water quality features on seasonal prediction while keeping other variables constant. RF achieved the highest accuracy of 79%, and XGBoost was about 77% among the models. The achieved prediction accuracies indicate a robust capability to capture key seasonal and spatial changes in river water quality. At this performance level, the models are effective in identifying conditions associated with deteriorated water quality and potential exceedances of guideline-based thresholds established by the World Health Organization (WHO) and Bangladesh water quality standards, supporting timely assessment and management interventions. The SHAP analysis demonstrated TDS, alkalinity, and EC as the top feature drivers of seasonal differences, providing insight into the interplay between chemical composition and climate. The results of the study have the potential to accurately depict the seasonal patterns in river water quality using AI approaches.

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