Temporal Dynamics and Predictive Modelling of Streamflow and Water Quality Using Advanced Statistical and Ensemble Machine Learning Techniques
Article
Article Title | Temporal Dynamics and Predictive Modelling of Streamflow and Water Quality Using Advanced Statistical and Ensemble Machine Learning Techniques |
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Article Category | Article |
Authors | Farzana, Syeda Zehan, Paudyal, Dev Raj, Chadalavada, Sreeni and Alam, Md Jahangir |
Journal Title | Water |
Journal Citation | 16 (15) |
Article Number | 2107 |
Number of Pages | 18 |
Year | 2024 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2073-4441 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/w16152107 |
Web Address (URL) | https://www.mdpi.com/2073-4441/16/15/2107 |
Abstract | Changes in water quality are closely linked to seasonal fluctuations in streamflow, and a thorough understanding of how these variations interact across different time scales is important for the efficient management of surface water bodies such as rivers, lakes, and reservoirs. The aim of this study is to explore the potential connection between streamflow, rainfall, and water quality and propose an optimised ensemble model for the prediction of a water quality index (WQI). This study modelled the changes in five water quality parameters such as ammonia nitrogen (NH3-N), phosphate (PO43−), pH, turbidity, total dissolved solids (TDS), and their associated WQI caused by rainfall and streamflow. The analysis was conducted across three temporal scales, weekly, monthly, and seasonal, using a generalised additive model (GAM) in Toowoomba, Australia. TDS, turbidity, and WQI exhibited a significant nonlinear variation with the changes in streamflow in the weekly and monthly scales. Additionally, pH demonstrated a significant linear to weakly linear correlation with discharge across the three temporal scales. For the accurate prediction of WQI, this study proposed an ensemble model integrating an extreme gradient boosting (XGBoost) and Bayesian optimisation (BO) algorithm, using streamflow as an input across the same temporal scales. The results for the three temporal scales provided the best accuracy of monthly data, based on the accuracy metrics R2 (0.91), MAE (0.20), and RMSE (0.42). The comparison between the test and predicted data indicated that the prediction model overestimated the WQI at some points. This study highlights the efficiency of integrating rainfall, streamflow, and water quality correlations for WQI prediction, which can provide valuable insights for guiding future water management strategies in similar catchment areas, especially amidst changing climatic conditions. |
Keywords | streamflow; Bayesian optimisation; XGBoost regressor; generalised additive model; water quality |
Article Publishing Charge (APC) Amount Paid | 2600 |
Article Publishing Charge (APC) Funding | Researcher |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400513. Water resources engineering |
401302. Geospatial information systems and geospatial data modelling | |
Byline Affiliations | School of Surveying and Built Environment |
School of Engineering | |
Murray-Darling Basin Authority, Australia |
https://research.usq.edu.au/item/z88y9/temporal-dynamics-and-predictive-modelling-of-streamflow-and-water-quality-using-advanced-statistical-and-ensemble-machine-learning-techniques
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