AIMC Topic: Rivers

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Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique.

The Science of the total environment Sep 29, 2024
Eutrophication is a major cause of water quality degradation in South Korea, owing to severe algal blooms. To manage eutrophication, the South Korean government provided the Trophic State Index (TSIko), which was revised according to Carlson's TSI. T...
Rivers Spatio-Temporal Analysis Deep Learning Environmental Monitoring Water Quality Republic of Korea Eutrophication Neural Networks, Computer
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Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

Environmental science and pollution research international Sep 24, 2024
The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple point...
Chlorophyll A Support Vector Machine Remote Sensing Technology Rivers Chlorophyll Republic of Korea Environmental Monitoring Machine Learning
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Integrating deep learning techniques for effective river water quality monitoring and management.

Journal of environmental management Sep 19, 2024
Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality param...
Deep Learning Environmental Monitoring Neural Networks, Computer Water Quality Rivers
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Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks.

Environmental research Sep 14, 2024
Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (...
Neural Networks, Computer Environmental Monitoring Illinois Water Quality Rivers Water Pollutants, Chemical Oxygen
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Detecting floating litter in freshwater bodies with semi-supervised deep learning.

Water research Sep 11, 2024
Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expen...
Deep Learning Supervised Machine Learning Rivers Environmental Monitoring Fresh Water
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A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures.

PloS one Sep 6, 2024
Enumeration of Campylobacter from environmental waters can be difficult due to its low concentrations, which can still pose a significant health risk. Spectrophotometry is an approach commonly used for fast detection of water-borne pollutants in wate...
Campylobacter Water Microbiology Spectrophotometry Machine Learning Rivers
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Forecasting for Haditha reservoir inflow in the West of Iraq using Support Vector Machine (SVM).

PloS one Sep 6, 2024
Accurate inflow forecasting is an essential non-engineering strategy to guarantee flood management and boost the effectiveness of the water supply. As inflow is the primary reservoir input, precise inflow forecasting may also offer appropriate reserv...
Water Supply Iraq Rivers Forecasting Support Vector Machine Seasons
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Hybrid deep learning based prediction for water quality of plain watershed.

Environmental research Sep 2, 2024
Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain wat...
Deep Learning Models, Theoretical Water Quality Rivers Environmental Monitoring Bayes Theorem
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Environmental water quality prediction based on COOT-CSO-LSTM deep learning.

Environmental science and pollution research international Aug 28, 2024
Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) m...
Animals Water Quality Models, Theoretical Rivers Deep Learning Environmental Monitoring India
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Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study in the USA.

Environmental science and pollution research international Aug 13, 2024
The Colorado River has experienced a significant streamflow reduction in recent decades due to climate change, resulting in pronounced hydrological droughts that pose challenges to the environment and human activities. However, current models struggl...
United States Models, Theoretical Rivers Machine Learning Climate Change Droughts Forecasting
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