Statistical analysis, machine learning modeling, and text analytics of aggregation attachment efficiency: Mono and binary particle systems

Gomez-Flores, Allan; Bradford, Scott A.; Hong, Gilsang; Kim, Hyung-Seok

doi:10.1016/j.jhazmat.2023.131482

Cited by 5 publications

(2 citation statements)

References 98 publications

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“…As suggested by systems theory, ANN-based recomposition complements traditional computational decomposition and involves organizing mixture components to understand how they dynamically work together to produce specific behaviors, especially through orchestrated mechanisms comprising feedback loops and including the role of noise and interactions with the environment. In fact, ANN graph representations of chemical mixtures demonstrate exceptional flexibility (up to hypergraphs) and expressivity for complex system analysis, visualization, interpretation, and transdisciplinary communication. − Combining them with probabilistic models and natural language processing may enable multifaceted deciphering of intricate relationships within a knowledge graph. , The concept of “fluid spatiality” enhances our understanding of networks by introducing dynamics into the connections, transitioning from “clocks” to “clouds” . Probabilistic Graph Models provide such a framework, elements within categories being treated as random variables with conditional dependencies …”

Section: Lessons Learned and Outlookmentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the ’recomposition’ of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure–Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

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Section: Lessons Learned and Outlookmentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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“…A wide range of empirical models are used in hydrological modeling practice. The "black box" type models [15,16] are built using the identification method, i.e., based on input and output observations. The structure of the model is determined by the type of operator that ensures the correspondence of actual and calculated data in the closing section of the basin.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Modeling of Suspended Sediment Yield Dependence on Environmental Conditions

Yermolaev

Mukharamova

2023

Water

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This paper describes the modelling of suspended sediment yield in a plains region in the European part of Russia (EPR) and its prediction for ungauged catchments. The studied plains area, excluding the Caucasus and Ural Mountains, covers 3.5 × 106 km2 of the total area of about 3.8 × 106 km2. Multiple regression methods, such as a generalized linear model (GLM) and a generalized additive model (GAM), are used to construct the models. The research methodology is based on a catchment approach. There are 49,516 river basins with an average area of about 75 km2 in the plain regions. The suspended sediment yield geodatabase contains data from 385 gauging stations. The linear GLM model of suspended sediment yield explains about 50% and the GAM model about 65% of the data variability (R-squared adjusted). The models include mean slope steepness, percentage of arable land, runoff per unit area, catchment area, soil rank and catchment soil erodibility as significant predictors. They also include a zonal-sectoral gradient (the sum of active temperatures and the standard deviation of air temperature, or directly by geographic coordinates). A GAM model is trained to predict suspended sediment yields for unexplored areas of the area. The paper presents the results of extrapolating suspended sediment yield values to ungauged river basins in a plains region of the EPR. For the first time for such a large area, the models built and the use of the basin approach made it possible to predict runoff values for hydrologically unexplored river basins.

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