Computational simulation using machine learning models in prediction of CO2 absorption in environmental applications

Jin, Hulin; Andalib, Vahid; Bokov, Dmitry Olegovich; Kamal, Mehnaz; Alashwal, May; Ghazali, Sami; Algarni, Mohammed; Mamdouh, Amr

doi:10.1016/j.molliq.2022.119159

Cited by 14 publications

(2 citation statements)

References 76 publications

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“…The main ML algorithms include three types: (1) neural network algorithms, such as artificial neural network (ANN) [ 15 ], back propagation Neural Network (BPNN) [ 16 ], recurrent neural network (RNN) [ 17 ], and long short-term memory (LSTM) [ 18 ]. (2) ML regression algorithms, such as support vector regression (SVR) [ 19 ], and ridge regression (Ridge) [ 20 ]. (3) ML tree algorithms, such as random forest (RF) [ 21 ], and decision tree (DT) [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Deng,

Wang,

et al. 2024

Sensors

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The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.

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Section: Introductionmentioning

confidence: 99%

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Deng,

Wang,

et al. 2024

Sensors

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“…ML algorithms can also be optimized to increase their prediction accuracy. ML algorithms are used in a variety of fields, such as chemistry (Jin et al, 2022). Some hospitality researchers have also attempted to apply ML algorithms (Derdouri & Osaragi, 2021; Martinez‐Torres & Toral, 2019; Mitra, 2020).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Airbnb prices through machine learning

Tang,

Cheng,

Zhang

2023

Manage Decis Econ

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Achieving accurate pricing is critical for both peer‐to‐peer (P2P) accommodation platforms and hosts. An understanding of the determinants of prices on P2P platforms, such as Airbnb, can improve service quality and help make pricing more rational. In this study, machine learning (ML) was applied to P2P accommodation pricing prediction. Data from Airbnb listings in Sydney, Australia, was collected, and 10 ML algorithms were used to predict prices. Host data were divided into training and testing sets. A total of 35 variables, including price and 34 independent variables, were identified. The 10 algorithms were evaluated using the Student's t test, the root mean squared error, and the R2 value. The CatBoostRegressor algorithm had the best performance. According to the relative weights in the optimized CatBoostRegressor algorithm, the key factors affecting pricing are the maximum number of guests, the number of bedrooms, and whether the room is private. Platforms can use these results to share accurate rental pricing information with hosts. Registered hosts can obtain timely information regarding the house rental market to set reasonable prices.

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Computational modeling of Hg/Ni ions separation via MOF/LDH nanocomposite: Machine learning based modeling

Ibrahim

Elkaeed

Alnuwaiser

et al. 2022

Arabian Journal of Chemistry

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Computational simulation using machine learning models in prediction of CO2 absorption in environmental applications

Cited by 14 publications

References 76 publications

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Forecasting Airbnb prices through machine learning

Computational modeling of Hg/Ni ions separation via MOF/LDH nanocomposite: Machine learning based modeling

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