AIMC Topic: Rivers

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Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory.

PloS one Dec 9, 2024
Land use in urban agglomerations is the main source of carbon emissions, and reducing them and improving land use efficiency are the keys to achieving sustainable development goals (SDGs). To advance the literature on densely populated cities and hig...
Sustainable Development China Urbanization Cities Carbon Machine Learning Environmental Monitoring Rivers Algorithms Humans Neural Networks, Computer
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Feasibility of classification of drainage and river water quality using machine learning methods based on multidimensional data from a gas sensor array.

Annals of agricultural and environmental medicine : AAEM Dec 2, 2024
OBJECTIVE: The aim of the study is to verify whether the electronic nose system - an array of 17 gas sensors with a signal analysis system - is a useful tool for the classification and preliminary assessment of the quality of drainage water.
Feasibility Studies Water Quality Machine Learning Gases Neural Networks, Computer Rivers Poland Electronic Nose Environmental Monitoring
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Graphene FET biochip on PCB reinforced by machine learning for ultrasensitive parallel detection of multiple antibiotics in water.

Biosensors & bioelectronics Nov 30, 2024
Antibiotics like Ciprofloxacin (Cfx), tetracycline (Tet) and Tobramycin (Tob) are commonly used against a broad-spectrum of bacterial infection. Recent surge in their uptake through the presence of their residues in environmental water has been linke...
Tetracycline Tobramycin Machine Learning Transistors, Electronic Rivers Anti-Bacterial Agents Equipment Design Biosensing Techniques Ciprofloxacin Graphite Water Pollutants, Chemical Limit of Detection
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Simulation and prediction of sulfamethazine migration, transformation and risk diffusion during cross-media infiltration from surface water to groundwater driven by dynamic water level: Machine learning coupled HYDRUS-GMS model.

Journal of environmental management Nov 30, 2024
Seasonal water level fluctuations in rivers significantly influenced the cross-media migration, transformation, and risk diffusion of antibiotics from the vadose zone into groundwater. This study developed a coupled model integrating machine learning...
Groundwater Water Pollutants, Chemical Rivers Sulfamethazine Seasons Machine Learning
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Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea.

The Science of the total environment Nov 19, 2024
This study addresses the critical public health issue of fecal coliform contamination in the four major rivers in South Korea (Han, Nakdong, Geum, and Yeongsan rivers) by applying advanced machine learning (ML) algorithms combined with Explainable Ar...
Rivers Water Microbiology Environmental Monitoring Enterobacteriaceae Water Quality Republic of Korea Feces Artificial Intelligence Machine Learning Neural Networks, Computer
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Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique.

Journal of environmental management Nov 11, 2024
The development of cascading hydropower dams in river basins has significantly altered natural flow regimes in recent decades. This study investigates hydrological alterations caused by cascading hydropower dams in the Lancang-Mekong River Basin (LMR...
Hydrology Rivers Climate Change Machine Learning Environmental Monitoring Humans
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Development of a deep learning-based feature stream network for forecasting riverine harmful algal blooms from a network perspective.

Water research Nov 5, 2024
Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is r...
Rivers Cyanobacteria Forecasting Harmful Algal Bloom Deep Learning Republic of Korea Environmental Monitoring
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Deciphering the impact of cascade reservoirs on nitrogen transport and nitrate transformation: Insights from multiple isotope analysis and machine learning.

Water research Oct 16, 2024
Construction of cascade reservoirs has altered nutrient dynamics and biogeochemical cycles, thereby influencing the composition and productivity of river ecosystems. The Lancang River (LCR), characterized by its cascade reservoir system, presents unc...
Machine Learning Isotopes Nitrates Rivers Environmental Monitoring Nitrogen
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Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management.

Journal of environmental management Oct 15, 2024
River water quality continues to deteriorate under the coupled effects of climate change and human activities. Machine learning (ML) is a promising approach for analyzing water quality. Nevertheless, the spatiotemporal dynamics of river water quality...
Machine Learning China Nitrogen Phosphorus Water Quality Cities Environmental Monitoring Rivers
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Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network.

Marine pollution bulletin Oct 10, 2024
Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current automated monitoring technologies for detecting this litter face limitations in survey efficiency, c...
Remote Sensing Technology Rivers Deep Learning Environmental Monitoring
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