AIMC Topic: Rivers

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Machine learning-based analysis of heavy metal contamination in Chinese lake basin sediments: Assessing influencing factors and policy implications.

Ecotoxicology and environmental safety Aug 1, 2024
Sediments are important heavy metal sinks in lakes, crucial for ensuring water environment safety. Existing studies mainly focused on well-studied lakes, leaving gaps in understanding pollution patterns in specific basins and influencing factors.We c...
Lakes China Environmental Monitoring Machine Learning Metals, Heavy Geologic Sediments Environmental Policy Water Pollutants, Chemical Rivers
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Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.

Water research Jul 30, 2024
Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollutio...
China Rivers Models, Theoretical Water Pollution Biological Oxygen Demand Analysis Phosphorus Machine Learning Water Quality Environmental Monitoring Water Pollutants, Chemical
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Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes.

Journal of environmental management Jul 26, 2024
Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing ...
Hydrology Rivers Rain Models, Theoretical Environmental Monitoring Machine Learning Agriculture Humans
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An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods.

Journal of environmental management Jul 22, 2024
Deep learning models provide a more powerful method for accurate and stable prediction of water quality in rivers, which is crucial for the intelligent management and control of the water environment. To increase the accuracy of predicting the water ...
Water Quality Rivers Deep Learning Environmental Monitoring Phosphorus Models, Theoretical
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Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran.

Journal of environmental management Jul 15, 2024
Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Cho...
Machine Learning Environmental Monitoring Water Pollutants, Chemical Rivers Geologic Sediments Iran Biological Availability
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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 Jul 11, 2024
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...
Machine Learning Pharmaceutical Preparations Rivers Environmental Monitoring Water Pollutants, Chemical Wastewater Waste Disposal, Fluid
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Deep-Learning-Based detection of recreational vessels in an estuarine soundscape in the May River, South Carolina, USA.

PloS one Jul 8, 2024
This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), U...
Sound Rivers Acoustics South Carolina Estuaries Humans Recreation Ships Deep Learning
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Comprehensive river water quality monitoring using convolutional neural networks and gated recurrent units: A case study along the Vaigai River.

Journal of environmental management Jul 1, 2024
Effective monitoring of river water quality is required for long-term water resource management. Convolutional Neural Networks and Gated Recurrent Unit-based water quality monitoring (CNGRU-WQM) were used in this investigation to develop a comprehens...
Rivers Environmental Monitoring Water Quality Neural Networks, Computer
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Forecasting of compound ocean-fluvial floods using machine learning.

Journal of environmental management Jun 13, 2024
Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or...
Forecasting Models, Theoretical Neural Networks, Computer Machine Learning Floods Oceans and Seas Support Vector Machine Rivers
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Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

Journal of environmental management Jun 12, 2024
One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time seri...
Floods Water Movements Models, Theoretical Rivers Machine Learning Forecasting Neural Networks, Computer
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