Machine learning-based evolution of water quality prediction model: an integrated robust framework for comparative application on periodic return and jitter data

Environmental Pollution, Год журнала: 2025, Номер unknown, С. 125834 - 125834

Опубликована: Фев. 1, 2025

Язык: Английский

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Phytoplankton Communities’ Response to Thermal Stratification and Changing Environmental Conditions in a Deep-Water Reservoir: Stochastic and Deterministic Processes

Sustainability, Год журнала: 2024, Номер 16(7), С. 3058 - 3058

Опубликована: Апрель 6, 2024

Thermal stratification has become more extensive and prolonged because of global warming, this change had a significant impact on the distribution patterns phytoplankton communities. However, response community structures assembly processes to thermal is not fully understood. We predicted that structure communities would be affected by among water layers associated with environmental condition changes, reflecting certain in temporal spatial scales. Phytoplankton from Danjiangkou Reservoir were collected October 2021 July 2022 verify prediction. During sampling period, remained thermally stratified stability. The composition surface layer significantly differed both thermocline bottom layer. phenomenon pattern nitrogen phosphorus and, thus, structures. Deterministic greater influence layers. In contrast, stochastic prevalent community. within exhibited broader niche range than layers, showing notable dissimilarity Canonical correspondence analysis (CCA) revealed vertical distributions correlated NH4+-N, pH, temperature (WT). summary, study explained deep-water reservoirs during period. Additionally, explored potential using stratified-state under subtropical–warm temperate climate as indicators context warming.

Язык: Английский

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Predicting water quality in municipal water management systems using a hybrid deep learning model

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108420 - 108420

Опубликована: Апрель 23, 2024

Язык: Английский

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Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(11)

Опубликована: Окт. 3, 2024

Язык: 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, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 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.

Язык: Английский

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Mixture of experts leveraging informer and LSTM variants for enhanced daily streamflow forecasting

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132737 - 132737

Опубликована: Янв. 1, 2025

Язык: Английский

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A copula framework for depth-stratified water quality monitoring in reservoirs

Journal of Water Process Engineering, Год журнала: 2025, Номер 70, С. 107057 - 107057

Опубликована: Фев. 1, 2025

Язык: Английский

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

Journal of Environmental Management, Год журнала: 2025, Номер 377, С. 124719 - 124719

Опубликована: Фев. 28, 2025

Язык: Английский

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Copula-based dependency modelling of hydraulic properties for non-linear filtration through porous media

Powder Technology, Год журнала: 2025, Номер unknown, С. 121069 - 121069

Опубликована: Апрель 1, 2025

Язык: Английский

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Dynamic Modelling, Simulation, and Sensitive Analysis of Lead Removal in a Fixed-Bed Adsorption Column using Waste-Based Materials

IOP Conference Series Earth and Environmental Science, Год журнала: 2024, Номер 1368(1), С. 012009 - 012009

Опубликована: Июнь 1, 2024

Abstract This study focuses on dynamic modelling and numerical simulation of lead removal from contaminated water using a fixed-bed adsorption column packed with waste-based adsorbents. The pressing need for efficient sustainable treatment methods, particularly heavy metal removal, underscores the significance this research. Lead contamination in sources poses severe health risks, necessitating development effective strategies. present investigation centres comprehensive mathematical model that considers critical parameters, including column’s physical dimensions, flow rate, initial concentration, rate constant, adsorbent density. is expressed as partial differential equation (PDE) describing temporal spatial evolution concentration along column. To solve PDE, method lines, powerful technique discretises domain handles resulting system ordinary equations (ODEs) an adaptive solver, employed. Following that, effect factors process are evaluated by sensitive analysis approach. Simulations conducted to elucidate intricate dynamics over time height. approach enables prediction profiles within at various intervals, providing crucial insights into behavior process.

Язык: Английский

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