Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes

Ganesh, N.; Jain, Paras; Choudhury, Amitava; Dutta, Prasun; Kalita, Kanak; Barsocchi, Paolo

doi:10.3390/pr9112095

Cited by 50 publications

(9 citation statements)

References 32 publications

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“…RFR is a machine learning based regression method. Its foundation lies in the bagging and random subspace methods [17]. During the training phase, it creates multiple decision trees and uses the class average as a prediction for all of them.…”

Section: Random Forest Regression (Rfr)mentioning

confidence: 99%

Rainfall prediction model in Semarang City using machine learning

Usman

Widodo

Adi

et al. 2023

IJEECS

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The erratic distribution of rainfall greatly affects people's daily activities, especially in Semarang City, so it is necessary to predict rainfall. Correct prediction of rainfall can improve community preparedness in dealing with natural disasters. Algorithms for machine learning and data mining have been extensively utilized in research involving rainfall data from various regions. The primary objectives of this study are to find the best regression algorithm and use machine learning algorithms to predict rainfall in Semarang. The dataset used is daily rainfall data for the City of Semarang from the meteorological, climatological, and geophysical agency (BMKG). Machine learning algorithms such as multiple linear regression, random forest regression, and replicated neural networks will be used to conduct regression analysis on this dataset. The mean absolute error and Root mean squared error techniques are utilized to evaluate the performance of machine learning algorithms. With an error rate of 13.055 for root mean squared error (RMSE) and 6.621 for mean absolute error (MAE), the results of the research indicate that the performance of the neural network algorithm is superior to that of other algorithms.

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Section: Random Forest Regression (Rfr)mentioning

confidence: 99%

Rainfall prediction model in Semarang City using machine learning

Usman

Widodo

Adi

et al. 2023

IJEECS

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“…Similar to MLP, RF regression was also demonstrated to perform well with nonlinear characteristics between input features and output, e.g., on fluid flow application researched by Dutta and coworkers. 33 Neural networks (NNs) or artificial neural networks (ANNs) are common methods for developing nonlinear classification or regression. 34 Inspired by a mechanism of the human brain, neurons are used for basic structure in a network and stack as a layer.…”

Section: System Model Approachmentioning

confidence: 99%

“…The authors emphasized that the successful modeling obtained was due to the model suitability to nonlinear relationships between a set of input features and the output. Similar to MLP, RF regression was also demonstrated to perform well with nonlinear characteristics between input features and output, e.g., on fluid flow application researched by Dutta and co-workers …”

Section: Fluid Displacement Experiments and System Model Approachmentioning

confidence: 99%

Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Suetrong,

Tosuai,

Vo Thanh

et al. 2024

Energy Fuels

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Immiscible fluid−fluid displacement dynamics is a crucial element to understanding and engineering many subsurface flow applications, including enhanced oil recovery and carbon dioxide geological sequestration. Although there are several interfacial properties that govern such a displacement dynamic, the wettability has been considered a dominant factor. Owing to its complex coaffinity among the three phases (i.e., solid−fluid−fluid) and difficulty to be characterized accurately and efficiently, the wettability (defined as the contact angle: θ) determination is of interest in the current study with aim toward machine learning (ML) approach.In the current research, four experimental packages of fluid displacement at 1D capillary scale served as data sets for ML examination on the θ predictability. Via digital image processing, fluid traveling length at a given time was extracted, and the theoretical θ was calculated as ground truth for the modeling, with input features being fluid traveling length, displacing velocity, and the interfacial tension. Random forest (RF) and multilayer perception (MLP) were selected for the modeling due to their appropriate characteristics to the investigated data (being nonlinear relation). The prediction results showed that RF apparently outperformed MLP on the θ prediction, reflecting its best ability to manage missing values and outliers. Although more input features analyzed (from two to three features) did yield a better prediction, the best model remains RF. Sensitivity on the key parameters of displacing velocity and the interfacial tension was also analyzed, where the results confirm the model prediction agreement with theories. The study demonstrated how ML model can be an alternative tool to elucidate the fluid displacement in subsurface, with additional potential for autonomously improving the deep underground flows, converging a new concept of "augmented" artificial intelligence.

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“…Random forest regression is a kind of regression approach that is based on machine learning [28]. The RF algorithm, as described by Breiman [13], employs ensemble learning to enhance prediction accuracy by integrating multiple trees.…”

Section: A Random Forestmentioning

confidence: 99%

Integration of Effective Models to Provide a Novel Method to Identify the Future Trend of Nikkei 225 Stocks Index

Zhu,

2024

IJACSA

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The stock market refers to a financial market in which individuals and institutions engage in the buying and selling of shares of publicly listed firms. The valuation of stocks is influenced by the interplay between the forces of supply and demand. The act of allocating funds to the stock market entails a certain degree of risk, while it presents the possibility of substantial gains over an extended period. The task of predicting stock prices in the securities market is further complicated by the presence of non-stationary and non-linear characteristics in financial time series data. While traditional techniques have the potential to enhance the accuracy of forecasting, they are also associated with computational complexities that might lead to an elevated occurrence of prediction mistakes. This is the reason why the financial industry has seen a growing prevalence of novel methods, particularly in the stock market. This work introduces a novel model that effectively addresses several challenges by integrating the random forest methodology with the artificial bee colony algorithm. In the current study, the hybrid model demonstrated superior performance and effectiveness compared to the other models. The proposed model exhibited optimum performance and demonstrated a significant degree of effectiveness with low errors. The efficiency of the predictive model for stock price forecasts was established via the analysis of data obtained from the Nikkei 225 index. The data included the timeframe from January 2013 to December 2022. The results reveal that the proposed framework demonstrates efficacy and reliability in evaluating and predicting the price time series of equities. The empirical evidence suggests that, when compared to other current methodologies, the proposed model has a greater degree of accuracy in predicting outcomes.

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Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes

Cited by 50 publications

References 32 publications

Rainfall prediction model in Semarang City using machine learning

Rainfall prediction model in Semarang City using machine learning

Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Integration of Effective Models to Provide a Novel Method to Identify the Future Trend of Nikkei 225 Stocks Index

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