AIMC Topic: Water Movements

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Spatiotemporal variations in Pearl River plume dispersion over the last decade based on VIIRS-derived sea surface salinity.

Marine pollution bulletin May 22, 2025
A river plume indicates the dispersion and transport path of pollutants from runoff, monitoring the spatiotemporal variation of river plume distribution from space is crucial for marine environmental governance. This study focuses on the Pearl River ...
China Environmental Monitoring Machine Learning Spatio-Temporal Analysis Salinity Satellite Imagery Water Movements Rivers Seawater
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A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning.

Environmental science and pollution research international Nov 28, 2024
Monthly runoff forecasting plays a critically supportive role in water resources planning and management. Various signal decomposition techniques have been widely applied to enhance the accuracy of monthly runoff forecasting. However, the forecasting...
Algorithms Forecasting Machine Learning Water Movements Models, Theoretical Environmental Monitoring
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Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments.

Marine pollution bulletin Nov 15, 2024
Several coastal regions require operational forecast systems for predicting the transport of pollutants released during marine accidents. In response to this need, surrogate models offer cost-effective solutions. Here, we propose a surrogate modeling...
Models, Theoretical Deep Learning Water Pollutants, Chemical Environmental Monitoring Water Movements
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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity.

PloS one Nov 14, 2024
Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intens...
Environment Water Movements Hydrology Soil Models, Theoretical Neural Networks, Computer Machine Learning
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A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system.

Water research Sep 12, 2024
Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that us...
Rain Water Movements Cities Fuzzy Logic Floods Models, Theoretical
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Fish-inspired tracking of underwater turbulent plumes.

Bioinspiration & biomimetics Sep 3, 2024
Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of...
Hydrodynamics Water Movements Biomimetics Swimming Oceans and Seas Animals Robotics Equipment Design Fishes Algorithms Computer Simulation
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Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks.

Water research Jul 25, 2024
Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potent...
Neural Networks, Computer Cities Models, Theoretical Water Movements
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The weighted multi-scale connections networks for macrodispersivity estimation.

Journal of contaminant hydrology Jul 5, 2024
Macrodispersivity is critical for predicting solute behaviors with dispersive transport models. Conventional methods of estimating macrodispersivity usually need to solve flow equations and are time-consuming. Convolutional neural networks (CNN) have...
Water Movements Models, Theoretical Groundwater Neural Networks, Computer
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Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

Journal of environmental management Jun 12, 2024
One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time seri...
Water Movements Rivers Floods Forecasting Models, Theoretical Neural Networks, Computer Machine Learning
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Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Journal of environmental management May 6, 2024
Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying ...
Models, Theoretical Rivers Machine Learning China Water Movements Hydrology Humans
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