TDS prediction with wavelet analysis and trend-seasonal decomposition and machine learning algorithms, Case Study: Karkheh River, Iran

Amini, Hossein, Fakheri, Farshid, Dar, Jaffer Yousuf, Shakeri, Reza, Nejati, Hossein, Lam, Man Yue ORCID: https://orcid.org/0000-0001-7259-968X, Zahraie, Banafsheh and Ahmadian, Reza ORCID: https://orcid.org/0000-0003-2665-4734 2025. TDS prediction with wavelet analysis and trend-seasonal decomposition and machine learning algorithms, Case Study: Karkheh River, Iran. Water Conservation Science and Engineering 10 (2) , 63. 10.1007/s41101-025-00390-z

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Abstract

Water quality assessment is definitely important, as the available water resources are highly stressed by population growth, climate change due to anthropogenic activities, and a significant change in consumption patterns. This study aims an innovative framework to predict the total dissolved solids (TDS) with more accuracy in the rivers, case study: “Karkheh River”, Iran, with the integration of signal analysis with machine learning algorithms. First, continuous wavelet transform (CWT) was applied to decompose the time series of water quality variables (e.g., Ca, HCO3, SO4, and Cl) into their trends, seasonality, and residuals, extracting features that capture temporal dynamics. These features served as input for non-linear machine learning models (XGBoost, Random Forest, Decision Tree) in differenct scenarios to compare which way of adding new feature would improve the model performance in terms of the TDS predictions. Adding new features characterized by only TDS signal analysis improved the TDS predictions and was compared with adding all variables signal characterization and compared with only using raw data to predict TDS level. Using a 50-year dataset from three different hydrometric stations, the models could achieve over 95% accuracy, and XGBoost outperformed others in terms of taking the advantage of the new extracted features from signals. The results indicates that signal-driven features significantly contribute to ccurately TDS prediction by 30% improvement in RMSE, and it can offer a scalable approach for real-time water quality monitoring in semi-arid river systems, which leads to a better early warning system for designing future mitigation strategies.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Springer
ISSN:	2366-3340
Date of First Compliant Deposit:	27 June 2025
Date of Acceptance:	17 June 2025
Last Modified:	27 Jun 2025 08:30
URI:	https://orca.cardiff.ac.uk/id/eprint/179358

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