AIMC Topic: Environmental Monitoring

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Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach.

Journal of environmental management
Analyzing the impacts of climate change (CC) and human activities (HA) on hydrological events is important for water resource management. This study quantifies the impacts of CC and HA on runoff and hydrological drought characteristics (HDC) in the Y...

Assessing potential toxic metal threats in tea growing soils of India with soil health indices and machine learning technologies.

Environmental monitoring and assessment
This study explores the impact of potentially toxic metals (PTMs) contamination in Indian tea-growing soils on ecosystems, soil quality, and human health using machine learning and statistical analysis. A total of 148 surface soil samples were collec...

Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.

PloS one
This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their develo...

Reimagining the Kendall plot: using N and O of nitrate and advanced machine learning to improve N pollutant source classification.

Isotopes in environmental and health studies
Nitrate () pollution is a serious water quality issue in many countries due to contamination of lakes, rivers, and aquifers by intensive agriculture practices and inadequate wastewater management. Nitrate pollution and associated cultural eutrophicat...

Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland.

Journal of environmental management
For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be processed from point inventories to surface predictive maps. For large-scale predictive mapping, very fe...

The spatiotemporal evolution of dissolved-phase NAPL plumes revealed by the integrated groundwater quality and machine learning models.

Water research
Rapid prediction of dissolved-phase contamination plume distributions is crucial for emergency remediation of aquifers contaminated with non-aqueous phase liquids (NAPLs). However, collecting and analyzing contaminated groundwater samples is expensiv...

Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications.

Environment international
Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targ...

Sentinel-2 imagery coupled with machine learning to modelling water turbidity in the Doce River Basin, Brazil.

Environmental monitoring and assessment
Remote sensing and machine learning are techniques that can be used to monitor water quality properties, surpassing the limitations of the conventional techniques. Turbidity is an important water quality property directly influenced by the Fundão dam...

Forecasting the concentration of the components of the particulate matter in Poland using neural networks.

Environmental science and pollution research international
Air pollution is a significant global challenge with profound impacts on human health and the environment. Elevated concentrations of various air pollutants contribute to numerous premature deaths each year. In Europe, and particularly in Poland, air...

Smart waste management and air pollution forecasting: Harnessing Internet of things and fully Elman neural network.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
As the Internet of things (IoT) continues to transform modern technologies, innovative applications in waste management and air pollution monitoring are becoming critical for sustainable development. In this manuscript, a novel smart waste management...