Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121259 - 121259

Published: June 1, 2024

Language: Английский

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Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124121 - 124121

Published: Jan. 15, 2025

Language: Английский

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Comparative analysis of correlation and causality inference in water quality problems with emphasis on TDS Karkheh River in Iran

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 22, 2025

Abstract Water quality management is a critical aspect of environmental sustainability, particularly in arid and semi-arid regions such as Iran where water scarcity compounded by degradation. This study delves into the causal relationships influencing quality, focusing on Total Dissolved Solids (TDS) primary indicator Karkheh River, southwest Iran. Utilizing comprehensive dataset spanning 50 years (1968–2018), this research integrates Machine Learning (ML) techniques to examine correlations infer causality among multiple parameters, including flow rate (Q), Sodium (Na + ), Magnesium (Mg 2+ Calcium (Ca Chloride (Cl − Sulfate (SO 4 2− Bicarbonates (HCO 3 pH. For modeling causation, “Back door linear regression” approach has been considered which establishes stable interpretable framework inference clear assumptions. Predictive was used show difference between correlation causation along with interpretability make predictive model transparent. does not report variables it showed Mg contributing target while findings reveal that TDS predominantly positive influenced Mg, Na, Cl, Ca SO , HCO pH exerting negative (inverse) effects. Unlike correlations, demonstrate directional often unequal influences, highlighting driver levels. novel application ML-based provides cost-effective time-efficient alternative traditional experimental methods. The results underscore potential ML-driven analysis guide resource policy-making. By identifying key drivers TDS, proposes targeted interventions mitigate deterioration. Moreover, insights gained lay foundation for developing early warning systems, ensuring proactive sustainable similar hydrological contexts.

Language: Английский

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Developing a real-time water quality simulation toolbox using machine learning and application programming interface

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124719 - 124719

Published: Feb. 28, 2025

Language: Английский

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