AIMC Topic: Geologic Sediments

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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...
Algorithms Models, Theoretical Iran Geologic Sediments Ecosystem Deep Learning
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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...
Sulfur Isotopes Mars Exobiology Algorithms Trace Elements Machine Learning Iron Geologic Sediments Sulfides Earth, Planet Extraterrestrial Environment
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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...
Geologic Sediments Environmental Monitoring Water Pollutants, Chemical Wetlands Metals, Heavy Machine Learning
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Machine learning-based analysis of heavy metal contamination in Chinese lake basin sediments: Assessing influencing factors and policy implications.

Ecotoxicology and environmental safety Aug 1, 2024
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...
Lakes China Machine Learning Rivers Metals, Heavy Geologic Sediments Environmental Monitoring Water Pollutants, Chemical Environmental Policy
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Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran.

Journal of environmental management Jul 15, 2024
Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Cho...
Iran Environmental Monitoring Biological Availability Geologic Sediments Machine Learning Water Pollutants, Chemical Rivers
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Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability.

Environmental science and pollution research international Jul 11, 2024
The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms ...
Geologic Sediments Ecosystem Environmental Monitoring Neural Networks, Computer Algorithms Machine Learning Forecasting
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Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge.

Environmental science & technology Jul 10, 2024
Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk assessment and remediation. Traditional experimental evaluation, constrained by prolonged, oxygen-free conditions, struggles to keep pace with emergin...
Machine Learning Geologic Sediments Biodegradation, Environmental Organic Chemicals Sewage Anaerobiosis
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A hybrid SWAT-ANN model approach for analysis of climate change impacts on sediment yield in an Eastern Himalayan sub-watershed of Brahmaputra.

Journal of environmental management Jun 20, 2024
The current study focuses on analyzing the impacts of climate change and land use/land cover (LULC) changes on sediment yield in the Puthimari basin, an Eastern Himalayan sub-watershed of the Brahmaputra, using a hybrid SWAT-ANN model approach. The a...
Geologic Sediments Models, Theoretical Neural Networks, Computer Environmental Monitoring Soil Climate Change
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Machine learning-based classification of petrofacies in fine laminated limestones.

Anais da Academia Brasileira de Ciencias May 20, 2024
Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoir units, limited vertical data impedes the efficient classification of lithologies. We present a tes...
Oil and Gas Fields Machine Learning Support Vector Machine Bayes Theorem Geologic Sediments
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Predicting reservoir sedimentation using multilayer perceptron - Artificial neural network model with measured and forecasted hydrometeorological data in Gibe-III reservoir, Omo-Gibe River basin, Ethiopia.

Journal of environmental management May 6, 2024
The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sustainable reservoir sedimentation planning and management and to minimize reservoir storage capacity loss. The main objective of this study was to ...
Geologic Sediments Neural Networks, Computer Hydrology Ethiopia Models, Theoretical Rivers Environmental Monitoring
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