AIMC Topic: Rivers

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Integrating deep learning techniques for effective river water quality monitoring and management.

Journal of environmental management
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...

Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks.

Environmental research
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 (...

Detecting floating litter in freshwater bodies with semi-supervised deep learning.

Water research
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...

A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures.

PloS one
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...

Forecasting for Haditha reservoir inflow in the West of Iraq using Support Vector Machine (SVM).

PloS one
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...

Hybrid deep learning based prediction for water quality of plain watershed.

Environmental research
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...

Environmental water quality prediction based on COOT-CSO-LSTM deep learning.

Environmental science and pollution research international
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...

Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study in the USA.

Environmental science and pollution research international
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...

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

Ecotoxicology and environmental safety
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...

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.

Water research
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...