AIMC Topic: Rivers

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Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia.

Environmental monitoring and assessment Feb 4, 2025
Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning id...
Remote Sensing Technology Rivers Satellite Imagery Environmental Monitoring Neural Networks, Computer Ethiopia Machine Learning Algorithms Agriculture Droughts
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Integrated machine learning-based optimization framework for surface water quality index comparing coastal and non-coastal cases of Guangxi, China.

Marine pollution bulletin Feb 3, 2025
In this study, an optimized comprehensive water quality index (WQI) model framework is developed, which combines advanced machine learning technology to compare different types of surface water quality assessment. The proposed framework enhancement e...
Environmental Monitoring Water Quality Rivers Machine Learning China
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Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed.

Journal of environmental management Jan 31, 2025
Nutrient pollution caused by excessive total nitrogen (TN) and total phosphorus (TP) is a significant environmental challenge globally, threatening water quality and ecosystem health. This study investigates the interplay between rainfall, topography...
Rivers Rain Environmental Monitoring Humans Water Pollutants, Chemical Nutrients Machine Learning Phosphorus Nitrogen Water Quality
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Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate-N concentrations in the lower stretch of Ganga River, India.

Environmental science and pollution research international Jan 30, 2025
Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshw...
Rivers Machine Learning Ecosystem India Environmental Monitoring Nitrogen Water Pollutants, Chemical Nitrates Water Quality
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High-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model.

Journal of environmental management Jan 29, 2025
Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathem...
Machine Learning Algorithms China Neural Networks, Computer Gravitation Hydrology Groundwater Environmental Monitoring Rivers
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Spatio-temporal analysis of litterfall load in the lower reaches of Qarqan and Tarim rivers using BP neural networks.

Scientific reports Jan 7, 2025
Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Li...
Neural Networks, Computer Forests Rivers China Environmental Monitoring Plant Leaves Ecosystem Spatio-Temporal Analysis
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Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.

Chemosphere Dec 31, 2024
Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, l...
Machine Learning Environmental Monitoring Support Vector Machine Water Pollutants, Chemical Rivers Pesticides
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Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Environment international Dec 24, 2024
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts...
Water Microbiology Environmental Monitoring Ecosystem Microbiota Human Activities RNA, Ribosomal, 16S Humans Phylogeny Water Pollution Machine Learning China Rivers
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Balanced hydropower and ecological benefits in reservoir-river-lake system: An integrated framework with machine learning and game theory.

Journal of environmental management Dec 17, 2024
The negative impacts of large hydroelectric reservoirs on downstream ecosystems have attracted worldwide attention. Few attempts have been made to dynamically predict ecological benefits and rationally negotiation in the reservoir-river-lake (RRL) sy...
Rivers Game Theory Machine Learning Ecosystem Lakes
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Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River.

Microbiology spectrum Dec 9, 2024
Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods often rely on physical evidence and circumstantial clues, but the emerging field of forensic microb...
Mice Bacteria RNA, Ribosomal, 16S Microbiota Rivers Machine Learning Animals Lung Drowning Water Microbiology Forensic Sciences
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