Temporal Dynamics and Predictive Modelling of Streamflow and Water Quality Using Advanced Statistical and Ensemble Machine Learning Techniques

Article


Farzana, Syeda Zehan, Paudyal, Dev Raj, Chadalavada, Sreeni and Alam, Md Jahangir. 2024. "Temporal Dynamics and Predictive Modelling of Streamflow and Water Quality Using Advanced Statistical and Ensemble Machine Learning Techniques." Water. 16 (15). https://doi.org/10.3390/w16152107
Article Title

Temporal Dynamics and Predictive Modelling of Streamflow and Water Quality Using Advanced Statistical and Ensemble Machine Learning Techniques

Article CategoryArticle
AuthorsFarzana, Syeda Zehan, Paudyal, Dev Raj, Chadalavada, Sreeni and Alam, Md Jahangir
Journal TitleWater
Journal Citation16 (15)
Article Number2107
Number of Pages18
Year2024
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2073-4441
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 Paid2600
Article Publishing Charge (APC) FundingResearcher
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400513. Water resources engineering
401302. Geospatial information systems and geospatial data modelling
Byline AffiliationsSchool of Surveying and Built Environment
School of Engineering
Murray-Darling Basin Authority, Australia
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Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2011. "Spatial data infrastructure convergence: building spatial data infrastructure bridges to address climate change." Joshi, P. K. and Singh, T. P. (ed.) Geoinformatics for climate change studies. New Delhi, India. TERI Press. pp. 377-392
Pro-poor land management in developing countries: a case study in Nepal and India
Paudyal, Dev Raj. 2008. Pro-poor land management in developing countries: a case study in Nepal and India. Copenhagen, Denmark. International Federation of Surveyors (FIG).
A regional collaborative network to improve spatial information sharing in Australia
Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2010. "A regional collaborative network to improve spatial information sharing in Australia." 12th Global Spatial Data Infrastructure Association World Conference (GSDI 12). Singapore 19 - 22 Oct 2010 Melbourne, Australia.
Spatial data infrastructure for pro-poor land management
Paudyal, Dev Raj and McDougall, Kevin. 2010. "Spatial data infrastructure for pro-poor land management." Staiger, Rudolf (ed.) 24th International Federation of Surveyors International Congress (FIG 2010): Facing the Challenges - Building the Capacity. Sydney, Australia 11 - 16 Apr 2010 Sydney, Australia.
Spatial data infrastructure for catchment management: a case study of Condamine Catchment, Australia
Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2009. "Spatial data infrastructure for catchment management: a case study of Condamine Catchment, Australia." Indian Cartographer. 28, pp. 439-444.
Building SDI bridges for catchment management
Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2009. "Building SDI bridges for catchment management." van Loenen, B., Besemer, J. W. J. and Zevenbergen, J. A. (ed.) SDI convergence: research, emerging trends, and critical assessment. Delft, Netherlands. Netherlands Geodetic Commission. pp. 265-279
Building spatial data infrastructure to support sustainable catchment management
Paudyal, Dev Raj and McDougall, Kevin. 2008. "Building spatial data infrastructure to support sustainable catchment management." McDougall, Kevin (ed.) Queensland Spatial Conference 2008. Surfers Paradise, Australia 17 - 19 Jul 2008 Brisbane, Australia.