AIMC Topic: Models, Theoretical

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Bipolar Fuzzy Pseudo-UP Ideal Of Pseudo-UP Algebra.

F1000Research Nov 18, 2024
In this paper, we apply the concept of bipolar fuzzy sets to pseudo-UP ideals in pseudo-UP algebras. We prove that the intersection of two bipolar fuzzy pseudo-UP ideals is also a bipolar fuzzy pseudo-UP ideal, while the union of two such ideals does...
Models, Theoretical Fuzzy Logic Algorithms
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Modelling and evaluation of mechanical performance and environmental impacts of sustainable concretes using a multi-objective optimization based innovative interpretable artificial intelligence method.

Journal of environmental management Nov 16, 2024
This study focuses on modelling sustainable concretes' mechanical and environmental properties with interpretable artificial intelligence-based automated rule extraction, management of waste materials, and meeting future prospects. In this context, 2...
Models, Theoretical Construction Materials Recycling Environment Artificial Intelligence Compressive Strength
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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...
Water Pollutants, Chemical Water Movements Models, Theoretical Environmental Monitoring Deep Learning
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Global greenhouse gas reduction forecasting via machine learning model in the scenario of energy transition.

Journal of environmental management Nov 15, 2024
Global warming is becoming increasingly serious, with greenhouse gas (GHGs) emissions identified as a principal contributor. In response to the climate crisis, many countries are actively transitioning to renewable energy. Therefore, it is crucial to...
Global Warming Bayes Theorem Greenhouse Gases Models, Theoretical Machine Learning Forecasting
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An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard.

Environmental science and pollution research international Nov 15, 2024
Soil erosion by wind poses a significant threat to various regions across the globe, such as drylands in the Middle East and Iran. Wind erosion hazard maps can assist in identifying the regions of highest wind erosion risk and are a valuable tool for...
Machine Learning Models, Theoretical Environmental Monitoring Soil Wind Iran Support Vector Machine
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A novel hybrid variable cross layer-based machine learning model improves the accuracy and interpretation of energy intensity prediction of wastewater treatment plant.

Journal of environmental management Nov 15, 2024
Energy intensity (EI) prediction in wastewater treatment plants (WWTPs) suffers from inaccuracy and non-interpretability due to poor data quality, complex mechanisms and various confounding variables. In this study, the novel hybrid variable cross la...
Wastewater Machine Learning Models, Theoretical Waste Disposal, Fluid Nitrogen
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Global forecasting of carbon concentration through a deep learning spatiotemporal modeling.

Journal of environmental management Nov 15, 2024
Given the global urgency to mitigate climate change, a key action is the development of effective carbon concentration reduction policies. To this end, an influential factor is the availability of accurate predictions of carbon concentration trends. ...
Deep Learning Models, Theoretical Neural Networks, Computer Forecasting Carbon Dioxide Climate Change Carbon Methane
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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...
Water Movements Neural Networks, Computer Hydrology Environment Soil Machine Learning Models, Theoretical
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Deep learning-based near-field effect correction method for Controlled Source Electromagnetic Method and application.

PloS one Nov 11, 2024
Addressing the impact of near-field effects in the Controlled Source Electromagnetic Method(CSEM) has long been a focal point in the realm of geophysical exploration. Therefore, we propose a deep learning-based near-field correction method for contro...
Deep Learning Neural Networks, Computer Electromagnetic Fields Electromagnetic Phenomena Models, Theoretical Computer Simulation
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Explainable artificial intelligence for reliable water demand forecasting to increase trust in predictions.

Water research Nov 9, 2024
The "EU Artificial Intelligence Act" sets a framework for the implementation of artificial intelligence (AI) in Europe. As a legal assessment reveals, AI applications in water supply systems are categorised as high-risk AI if a failure in the AI appl...
Water Supply Models, Theoretical Artificial Intelligence Machine Learning Forecasting
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