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Environmental Monitoring

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Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area.

Environmental geochemistry and health
Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of s...

Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method.

Marine pollution bulletin
The escalating growth of the global population has led to degraded water quality, particularly in seawater environments. Water quality monitoring is crucial to understanding the dynamic changes and implementing effective management strategies. In thi...

Discovering transformation products of pharmaceuticals in domestic wastewaters and receiving rivers by using non-target screening and machine learning approaches.

The Science of the total environment
Wastewater treatment plants (WWTPs) are an important source of pharmaceuticals in surface water, but information about their transformation products (TPs) is very limited. Here, we investigated occurrence and transformation of pharmaceuticals and TPs...

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Environmental monitoring and assessment
Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Theref...

Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye.

Environmental science and pollution research international
Groundwater resources in Bitlis province and its surroundings in Türkiye's Eastern Anatolia Region are pivotal for drinking water, yet they face a significant threat from fluoride contamination, compounded by the region's volcanic rock structure. To ...

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability.

Environmental science and pollution research international
The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms ...

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data.

The Science of the total environment
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils ...

A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The Science of the total environment
The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental f...

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments.

Water research
The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the an...

Classification machine learning to detect de facto reuse and cyanobacteria at a drinking water intake.

The Science of the total environment
Harmful algal blooms (HABs) or higher levels of de facto water reuse (DFR) can increase the levels of certain contaminants at drinking water intakes. Therefore, the goal of this study was to use multi-class supervised machine learning (SML) classific...