AIMC Topic: Geologic Sediments

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Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm.

Environmental monitoring and assessment Jun 25, 2025
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood man...
Algorithms Environmental Monitoring Neural Networks, Computer Rivers Geologic Sediments Water Pollutants, Chemical Turkey
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Artificial Intelligence-Enhanced Detection of Biogenicity Using Laboratory Specimens of Biologically and Microbially Induced Sedimentary Structures in a Controlled Experiment.

Astrobiology May 30, 2025
The search for traces of life can be based on the detection of specific signatures produced by microorganisms on sedimentary rocks. Microbially induced sedimentary structures (MISSs) develop under specific physicochemical conditions that are likely t...
Exobiology Neural Networks, Computer Artificial Intelligence Geologic Sediments Mars
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LucaPCycle: Illuminating microbial phosphorus cycling in deep-sea cold seep sediments using protein language models.

Nature communications May 26, 2025
Phosphorus is essential for life and critically influences marine productivity. Despite geochemical evidence of active phosphorus cycling in deep-sea cold seeps, the microbial processes involved remain poorly understood. Traditional sequence-based se...
Phylogeny Alkaline Phosphatase Geologic Sediments Archaea Phosphorus Seawater
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Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

Environmental monitoring and assessment Apr 26, 2025
The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analyzes historical changes in SSL and evaluates machine learning (ML) models for SSL prediction in the G...
Environmental Monitoring Bayes Theorem Humans Machine Learning Rivers Geologic Sediments
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Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach.

Journal of environmental management Mar 17, 2025
Suspended sediment load (SSL) refers to sediment particles, such as silt and clay, that are suspended in water. It plays a critical role in hydrology and water quality management, influencing factors such as water quality, river erosion, sedimentatio...
Extreme Learning Machines Rivers Geologic Sediments Environmental Monitoring Water Quality Machine Learning
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Machine learning-based prediction of unconfined compressive strength and contaminant leachability in dredged contaminated sediments for land reclamation projects.

Environmental science and pollution research international Mar 8, 2025
This research investigates the application of machine learning techniques for predicting unconfined compressive strength (UCS) and contaminant leachability in dredged contaminated sediments (DCS) with implications for land reclamation projects. Tradi...
Machine Learning Geologic Sediments Compressive Strength
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Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone.

Journal of the American Society for Mass Spectrometry Dec 19, 2024
The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mix...
Organic Chemicals Microscopy, Electron, Scanning Geologic Sediments Algorithms Machine Learning Spectrometry, Mass, Secondary Ion Spectrometry, X-Ray Emission Biomarkers
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Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

Environmental science and pollution research international Dec 14, 2024
This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-water...
Models, Theoretical Iran Geologic Sediments Ecosystem Deep Learning Algorithms
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A Machine-Learning Approach to Biosignature Exploration on Early Earth and Mars Using Sulfur Isotope and Trace Element Data in Pyrite.

Astrobiology Oct 25, 2024
We propose a novel approach to identify the origin of pyrite grains and distinguish biologically influenced sedimentary pyrite using combined sulfur isotope (δS) and trace element (TE) analyses. To classify and predict the origin of individual pyrit...
Sulfides Geologic Sediments Trace Elements Sulfur Isotopes Iron Mars Exobiology Earth, Planet Extraterrestrial Environment Machine Learning Algorithms
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Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest.

The Science of the total environment Aug 23, 2024
The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challengin...
Water Pollutants, Chemical Metals, Heavy Machine Learning Wetlands Geologic Sediments Environmental Monitoring
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