AIMC Topic: Rivers

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Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso.

Water science and technology : a journal of the International Association on Water Pollution Research May 2, 2024
With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on...
Machine Learning Forecasting Rivers Water Movements Hydrology China Models, Theoretical
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Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping.

Environmental science and pollution research international Apr 26, 2024
Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of thes...
Support Vector Machine Machine Learning Rivers Iran Algorithms Floods
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Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

Environmental science and pollution research international Apr 24, 2024
The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and ...
Rivers Geologic Sediments Environmental Monitoring Iran Neural Networks, Computer Deep Learning Models, Theoretical
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Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China.

Environmental science and pollution research international Apr 22, 2024
Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water environment limits the accurate understanding of its pollution prediction. Field observations were cond...
Metals, Heavy Geologic Sediments Water Pollutants, Chemical Rivers Estuaries Neural Networks, Computer Risk Assessment China Environmental Monitoring
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Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Water science and technology : a journal of the International Association on Water Pollution Research Apr 17, 2024
Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Comp...
Water Movements Machine Learning Canada Nonlinear Dynamics Models, Theoretical Rivers Regression Analysis
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Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research Apr 15, 2024
Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore...
Environmental Monitoring Water Pollutants, Chemical Water Pollution Rivers China Neural Networks, Computer Agriculture
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Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation.

The Science of the total environment Apr 10, 2024
Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanob...
Cyanobacteria Environmental Monitoring Machine Learning Spain Rivers Bayes Theorem Water Microbiology Water Quality
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Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins.

Journal of environmental management Apr 1, 2024
Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites...
Nitrates Rivers Deep Learning Environmental Monitoring Models, Statistical
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Graph neural network-based anomaly detection for river network systems.

F1000Research Mar 28, 2024
BACKGROUND: Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology.Anomaly d...
Water Quality Neural Networks, Computer Rivers Environmental Monitoring
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Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators.

Environmental science and pollution research international Mar 23, 2024
Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study describ...
Artificial Intelligence Rivers Environmental Monitoring Models, Theoretical Soft Computing Water Quality
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