Linear Regression Trees Research Articles

Data-Driven Models for Significant Wave Height Forecasting: Comparative Analysis of Machine Learning Techniques

Accurate prediction of significant wave height (SWH) is critical for coastal safety, marine operations, and disaster management. Traditional numerical models for wave prediction are computationally intensive and often lack accuracy, prompting a shift towards data-driven methods. This study explores the efficacy of machine learning (ML) models in forecasting SWH in the coastline of North Stradbroke Island, Queensland, Australia. Using a dataset spanning 2010 to 2022, the study employs wave characteristics such as maximum wave height (Hmax), wave periods (Tz, Tp), peak direction, and sea surface temperature (SST) as predictors. Three ML models—Linear Regression, Decision Tree, and Random Forest—were trained and evaluated using performance metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The Random Forest model demonstrated the best predictive accuracy with the lowest MSE (0.0074) and highest R² (0.958), outperforming both Linear Regression and Decision Tree models. This improvement in prediction accuracy supports the model's application for coastal management, ensuring better forecasting of wave conditions. The proposed approach shows significant advantages, including better handling of non-linearities and reduced computational costs compared to conventional numerical methods, such as SWAN and WAM, making it highly applicable for real-time wave forecasting, particularly for regions with complex coastal dynamics such as North Stradbroke Island.

Spatiotemporal variations of cropland phosphorus runoff loss in China

Quantitative assessment of cropland phosphorus (P) loss via surface runoff is essential for developing effective pollution mitigation strategies. In this study, we compiled 812 datasets from 114 peer-reviewed papers for cropland P loss across China. We then developed machine learning (ML) approaches to estimate temporal and spatial variations in P runoff loss across China from 1990 to 2020. Four prevalent ML models were considered, namely, multiple linear regression (MLR), random forest (RF), classification and regression trees (CART), and boosted regression trees (BRT). Among these four models, RF exhibited the highest predictive accuracy for both uplands (calibration: R2 = 0.86, n = 293; validation: R2 = 0.61, n = 96) and paddy fields (calibration: R2 = 0.88, n = 137; validation: R2 = 0.60, n = 44). According to RF, China’s croplands are estimated to have lost an average of 148 ± 27 Gg P yr−1 from 1990 to 2020, with uplands and paddy fields contributing 114 ± 26 Gg P yr−1 and 34 ± 4 Gg P yr−1, respectively. There was a significant increase in upland TP runoff loss over the study period (p < 0.001), whereas paddy field TP loss remained relatively constant. Regions in southern, eastern, and southwestern China, notably in Hainan, Guangxi, and Fujian provinces, were identified as hotspots of cropland TP runoff loss. Improved cropland management scenarios were predicted to reduce TP runoff loss by 1.4–11.8 %, with the best results obtained by minimizing runoff depth. To effectively mitigate TP runoff loss, an integrated management approach involving water, soil, and fertilizer is recommended. This study enhances quantitative understanding of cropland TP runoff loss in China, providing crucial insights for efficient cropland P management, which is key to managing nonpoint source pollution on a national level.

Role of letermovir therapeutic drug monitoring for cytomegalovirus prophylaxis in allogeneic hematopoietic stem cell transplantation recipients: a prospective study.

The role of therapeutic drug monitoring (TDM) in allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipients receiving letermovir has not yet been clarified. This study is to explore letermovir trough concentration (Cmin) correlation with its clinical efficacy and adverse events, and factors affecting its plasma concentrations. A prospective, non-interventional study was performed in allo-HSCT recipients receiving letermovir prophylaxis. Plasma concentrations were determined using high-performance liquid chromatography-tandem mass spectrometry. Data analysis was performed using logistic regression, linear regression, and classification and regression tree (CART) models. 701 trough concentrations from 71 recipients were included, uncovering pronounced intra- and inter-individual variability in letermovir Cmin. During 24-week follow-up, CMV infection incidence was 16.4%. A significant correlation was identified between letermovir Cmin and its clinical efficacy, and the CART model showed an increased risk of CMV infection when Cmin ≤ 2731 ng/mL. However, no clear correlation was found between Cmin and adverse events. Gastrointestinal graft-versus-host disease, cyclosporine Cmin, gender, and concomitant medications, including mycophenolate mofetil, ondansetron, caspofungin, and methylprednisolone, may impact letermovir Cmin. Additionally, coadministration with cyclosporine injection significantly decreased median letermovir Cmin compared with cyclosporine capsules (2311 vs. 3386 ng/mL). Moreover, with the extension of time post-transplant, trough concentrations of both cyclosporine and letermovir significantly decreased. TDM for letermovir may be beneficial in allo-HSCT recipients considering the variability in letermovir Cmin and its correlation with clinical efficacy. Moreover, drug interactions and the effects of changes in cyclosporine dosage forms or concentrations require careful monitoring for their effect on letermovir Cmin.

Enhancing and Explaining Energy Management in Smart Manufacturing Production via ML Within the Context of Industry 4.0

Objectives: This study aims to explore the integration of big data and machine learning to enhance energy efficiency in manufacturing, focusing on the opportunities presented by Industry 4.0 and cyber-physical production systems. Theoretical Framework: The research leverages supervised learning techniques to analyze and predict machine-specific energy consumption patterns, utilizing energy disaggregation as a foundational concept. Method: Various machine learning algorithms, including Lasso regression, Linear regression, and Decision Tree, will be employed to develop predictive models for energy usage. Additionally, Explainable Machine Learning (XML) techniques will be utilized to ensure interpretability and clarity in the prediction outcomes. Results and Discussion: The proposed framework aims to elucidate the relationship between equipment utility and energy consumption, providing comprehensible explanations that enhance decision-making processes within intelligent industrial environments. Research Implications: This work highlights the significant potential of XML in transforming machine learning applications in manufacturing, paving the way for improved energy efficiency and operational effectiveness. Originality/Value: The introduction of an innovative framework that combines machine learning with explainability in the context of energy consumption marks a valuable contribution to the fields of manufacturing and sustainable industry practices.

The Comprehensive Investigation for Covid-19 Trend Prediction Through Machine Learning and Deep Learning

Corona Virus Disease (Covid-19) has surely been a challenging problem to solve for the past few years. Due to the diversity in the form of dataset, it is essential to obtain accurate predictive results of Covid-19 trends. This paper analyzes different artificial intelligence methods used in Covid-19 trend prediction, including several machine learning and deep learning methods. More specifically, this work investigates linear regression, random forest, and decision trees in terms of machine learning and delves into Artificial Neural Network (ANN) as well as Long Short-Term Memory (LSTM) for deep learning. By comparing various past works, the effectiveness of machine learning and deep learning methods is achieved by their hidden algorithms, such as the Multiple Linear Regression (MLR) model for linear regression analysis. Incorporation with other models or methods is applied in deep learning. For example, Ensemble Empirical Mode Decomposition (EEMD) is included in ANN structure to decrease the noises within the Covid-19 datasets. Furthermore, the paper also inquiries into potential improvement of some drawbacks in predictive results for Covid-19 trends by reviewing related works of expert system and transfer learning as well as domain adaptation. The machine learning and deep learning models could provide accurate predictive results as a reference for related organizations to consider or establish insightful policies.

PREDICTIVE TECHNIQUES AND ARTIFICIAL INTELLIGENCE MODELS IN THE MANAGEMENT OF DEFAULTY PUBLIC DEBTS: COMPARISON BETWEEN LINEAR REGRESSION AND DECISION TREES

The study analyzed focuses on the application of predictive models, specifically Linear Regression and Decision Trees, for the management of defaulted debts in the public context of the United States. The main objective of the work is to compare the effectiveness of these models in predicting the compliance of debts with more than 120 days, assisting in directing these debts to the Treasury Offset Program (TOP), an essential initiative for the government's financial recovery. The problem that the study addresses is the need for effective management of defaulted public debts, seeking to ensure compliance with public financial policies that promote compliance and the adequate redirection of financial resources to the government. This is particularly important to ensure fiscal transparency and accountability of federal agencies. The methodology used in the study was quantitative, based on the analysis of data on eligible debts extracted from reports of the US Treasury. Linear Regression and Decision Tree models were applied, with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The study addressed financial and temporal variables to analyze the behavior of these debts and their compliance. The main results show that both models presented high accuracy in predictions, with Linear Regression showing a perfect fit (R² = 1) and Decision Trees standing out in capturing non-linear nuances of the data. The variable "Compliance Rate Amount" was identified as the most significant in the Decision Tree model, suggesting that the amount of the compliance rate is one of the most important factors in predicting the compliance of defaulted debts. This study offers valuable contributions to the field of public management, by demonstrating that the use of predictive models can help optimize debt recovery, improve fiscal transparency and contribute to more informed decision-making.

Interpretable Machine Learning: A Case Study on Predicting Fuel Consumption in VLGC Ship Propulsion

The integration of machine learning (ML) in marine engineering has been increasingly subjected to stringent regulatory scrutiny. While environmental regulations aim to reduce harmful emissions and energy consumption, there is also a growing demand for the interpretability of ML models to ensure their reliability and adherence to safety standards. This research highlights the need to develop models that are both transparent and comprehensible to domain experts and regulatory bodies. This paper underscores the importance of transparency in machine learning through a use case involving a VLGC ship two-stroke propulsion engine. By adhering to the CRISP-DM standard, we fostered close collaboration between marine engineers and machine learning experts to circumvent the common pitfalls of automated ML. The methodology included comprehensive data exploration, cleaning, and verification, followed by feature selection and training of linear regression and decision tree models that are not only transparent but also highly interpretable. The linear model achieved an RMSE of 23.16 and an MRAE of 14.7%, while the accuracy of decision trees ranged between 96.4% and 97.69%. This study demonstrates that machine learning models for predicting propulsion engine fuel consumption can be interpretable, adhering to regulatory requirements, while still achieving adequate predictive performance.

A Comparative Study of NIFTY-50 Prediction Using Linear Regression and Regression Tree Models

It is important for investors to predict the tendency of stock. However, the stock market fluctuation is too hard to predict. A single stock may be inflected by many elements, so it would be much more feasible to study a stock index. A stock index may be decided by many stocks, which means that it would not be effected by some only one company. In this passage, NIFTY-50 would be analyzed to make a prediction. The linear regression and regression tree are used to predict the close price of NIFTY-50. To make sure the data size is big enough, the data includes the price from 2000 to 2021. By comparing two models, a proper model to predict stock index would be found. After the comparison of the MSE and R square index, it is easy to find that the regression tree is the model much more reliable. However, there is still long way to go before researchers find way to predict stock price precisely.

Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models.

This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( ). The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.

Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

A Trombe wall-heating system is used to absorb solar energy to heat buildings. Different parameters affect the system performance for optimal heating. This study evaluated the performance of four machine learning algorithms—linear regression, k-nearest neighbors, random forest, and decision tree—for predicting the room temperature in a Trombe wall system. The accuracy of the algorithms was assessed using R² and RMSE values. The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance, with R² and RMSE values of 1 and 0. In contrast, linear regression and decision tree showed weaker performance. These findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems, enabling informed design decisions to enhance energy efficiency.

An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry

PurposeIn today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.Design/methodology/approachA questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.FindingsThe findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.Originality/valueThis study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

Noninvasive brain stimulation during EEG improves machine learning classification in chronic stroke.

In individuals with chronic stroke and hemiparesis, noninvasive brain stimulation (NIBS) may be used as an adjunct to therapy for improving motor recovery. Specific states of movement during motor recovery are more responsive to brain stimulation than others, thus a system that could auto-detect movement state would be useful in correctly identifying the most effective stimulation periods. The aim of this study was to compare the performance of different machine learning models in classifying movement periods during EEG recordings of hemiparetic individuals receiving noninvasive brain stimulation. We hypothesized that transcranial direct current stimulation, a form of NIBS, would modulate brain recordings correlating with movement state and improve classification accuracies above those receiving sham stimulation. Electroencephalogram data were obtained from 10 participants with chronic stroke and 11 healthy individuals performing a motor task while undergoing transcranial direct current stimulation. Eight traditional machine learning algorithms and five ensemble methods were used to classify two movement states (a hold posture and an arm reaching movement) before, during and after stimulation. To minimize compute times, preprocessing and feature extraction were limited to z-score normalization and power binning into five frequency bands (delta through gamma). Classification of disease state produced significantly higher accuracies in the stimulation (versus sham) group at 78.9% (versus 55.6%, p < 0.000002). We observed significantly higher accuracies when classifying stimulation state in the chronic stroke group (77.6%) relative to healthy controls (64.1%, p < 0.0095). In the chronic stroke cohort, classification of hold versus reach was highest during the stimulation period (75.2%) as opposed to the pre- and post-stimulation periods. Linear discriminant analysis, logistic regression, and decision tree algorithms classified movement state most accurately in participants with chronic stroke during the stimulation period (76.1%). For the ensemble methods, the highest classification accuracy for hold versus reach was achieved using low gamma frequency (30-50 Hz) as a feature (74.5%), although this result did not achieve statistical significance. Machine learning algorithms demonstrated sufficiently high movement state classification accuracy in participants with chronic stroke performing functional tasks during noninvasive brain stimulation. tDCS improved disease state and movement state classification in participants with chronic stroke.

OPTIMIZING RETAIL DEMAND FORECASTING: A PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS INCLUDING LSTM AND GRADIENT BOOSTING

Effective demand forecasting is vital for inventory management in retail. This study evaluates five machine learning models—Linear Regression (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting (GB), and Long Short-Term Memory (LSTM)—for predicting retail demand. Utilizing a dataset with transactional sales, promotions, calendar events, and external factors like weather and economic indicators, we applied rigorous preprocessing and feature engineering. Performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). Results show that LSTM outperforms other models with an MAE of 9.53, RMSE of 14.67, and R² of 0.90, excelling in capturing temporal dependencies and complex demand patterns. Gradient Boosting and Random Forest also performed well, while Linear Regression and Decision Tree Regressor showed limitations. This study highlights the effectiveness of advanced models, particularly LSTM, for enhancing demand forecasting accuracy and offers valuable insights for optimizing retail inventory and operations.

Robustness Investigation of ML Models for Stock Price Prediction During Market Volatility: A Case Study of the Japanese Stock Market Amidst the COVID-19 Pandemic

In the evolving realm of financial markets, precise stock price prediction remains a pivotal challenge. This paper investigates the efficacy of traditional machine learning models for stock price forecasting, particularly during the high volatility phase induced by the COVID-19 pandemic, using the Japanese stock market as a case study. This study utilized a dataset from the JPX Tokyo Stock Exchange Prediction competition, selecting two stocks at random to avoid bias. After standardizing the data for consistency, this study employed linear regression and decision trees to project stock prices, comparing their performance during stable periods and the economic upheaval of the pandemic. The analysis revealed that both models' predictive capabilities were compromised during the pandemic, with the decision tree model particularly underperforming. In normal times, linear regression showed improved accuracy for stock 1301 in 2021, while for stock 6758, the model's performance declined. These findings underscore the necessity for robust models that can withstand market turbulences like those observed during the COVID-19 crisis. The research contributes to both academic discussions on financial forecasting and practical strategies for market participants navigating complex stock environments. Future work will focus on developing more resilient forecasting methods to address these challenges.

Analyzing Stock Price Prediction Models: A Comparative Study of Linear Regression and Decision Trees During the COVID-19 Pandemic

Stock market forecasting plays a vital role in financial decision making. In a volatile and uncertain situation like the COVID-19 pandemic, it is important for us to compare the accuracy of different models for forecasting. This study compares the performance of the Linear Regression (LR) algorithm and the Decision Tree (DT) algorithm in predicting the stock price of Pfizer Inc. before and during a pandemic. The study evaluates the accuracy and stability of the predictions using datasets before and during the pandemic. The results show that the prediction accuracy of LR is better than that of DT in both cases, although both accuracies decrease during the pandemic. The results of the study emphasize the need to consider external factors in the selection of forecasting models and suggest some lessons for future research, leading to the selection of more stable and accurate models under turbulent market conditions. It is worth noting that overfitting occurs when a model learns to capture noise in the data rather than the underlying patterns, leading to reduced generalization performance on unseen data. Future research should address this by exploring techniques such as regularization or cross-validation to mitigate overfitting and enhance the robustness of predictive models in dynamic market environments.

Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective.

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

This study introduces machine learning (ML) algorithms to predict hydrogen (H2) thermodynamic properties for geological storage, focusing on its mixtures with natural gas, nitrogen (N2), and carbon dioxide (CO2). Employing 1167 data samples, this research utilizes multiple linear regression (MLR), random forest (RF), gradient boosting (GB), and decision tree (DT) models, enhanced by sensitivity analysis for feature engineering. GB model's superior accuracy and efficiency over conventional statistical linear regression methods. The finding reveals that the GB model achieves superior performance, with an R2 value of 0.9998 for thermal capacity and the lowest mean absolute error (MAE)/root mean square error (RMSE) across all H2 properties—density (1.51%, 2.06 kg/m3), viscosity (0.000078%, 0.000117 cp), thermal conductivity (0.000427%, 0.000849 W/m.K), and thermal capacity (8.79%, 26.06 J/g.K). Moreover, this ML-based approach not only demonstrates remarkable accuracy and efficiency but also suggests the potential of smart paradigms to further enhance the safety and transportability of underground hydrogen storage projects. Ultimately, this work will help the reservoir simulation at field scale to be more robust and reliable.

Degrees of Freedom: Search Cost and Self-consistency

Model degrees of freedom ( df ) is a fundamental concept in statistics because it quantifies the flexibility of a fitting procedure and is indispensable in model selection. To investigate the gap between df and the number of independent variables in the fitting procedure, Tibshirani (2015) introduced the search degrees of freedom ( sdf ) concept to account for the search cost during model selection. However, this definition has two limitations: it does not consider fitting procedures in augmented spaces and does not use the same fitting procedure for sdf and df . We propose a modified search degrees of freedom ( msdf ) to directly account for the cost of searching in either original or augmented spaces. We check this definition for various fitting procedures, including classical linear regressions, spline methods, adaptive regressions (the best subset and the lasso), regression trees, and multivariate adaptive regression splines (MARS). In many scenarios when sdf is applicable, msdf reduces to sdf . However, for certain procedures like the lasso, msdf offers a fresh perspective on search costs. For some complex procedures like MARS, the df has been pre-determined during model fitting, but the df of the final fitted procedure might differ from the pre-determined one. To investigate this discrepancy, we introduce the concepts of nominal df and actual df , and define the property of self-consistency, which occurs when there is no gap between these two df ’s. We propose a correction procedure for MARS to align these two df ’s, demonstrating improved fitting performance through extensive simulations and two real data applications.

Prediction of martensitic transformation start temperature of steel using thermodynamic model, empirical formulas, and machine learning models

Three methods are used to predict the martensitic transformation start temperature (Ms ) of steel. Based on the database containing 832 compositions and corresponding Ms data, prediction models are built, modified, and trained. Firstly, Ms was re-calculated by establishing a thermodynamic model to link the martensitic transformation driving force (Gibbs free energy difference of martensite and austenite) with resistance (elastic strain energy, plastic strain energy, interface energy, and shearing energy). Secondly, the existing Ms data is cleaned and re-predicted using traditional empirical formulas within different composition application ranges. Thirdly, four different algorithms in machine learning including random forest, k nearest neighbor, linear regression, and decision tree are trained to predict 832 new Ms values. By comparing the Ms results re-predicted by the mentioned three methods with the original Ms values, the accuracy is evaluated to identify the optimal prediction model.

Enhancing TikTok Account Performance with Data Pattern Identification

TikTok is a social media platform widely used to share information through short videos to achieve goals and interests in business, education, politics, government, and personal existence. Every activity on this platform is recorded and presented privately to each account owner. However, this data has not been utilized optimally to improve account performance. This research aims to offer a data analysis concept that integrates statistical and machine learning approaches to identify data patterns in each user's data collection, enabling the improvement of account performance. The approach utilizes Linear Regression, k-means, and Decision Tree methods. The results obtained show that the concept of identifying data patterns in TikTok account data has successfully developed a predictive model for video posts that can potentially increase total viewership, video plays, and audience engagement. This is achieved through optimizing video components such as captions, text, hashtags, sound genre, and video type. The outcome yielded a classification model that can predict capable component content to enhance account performance.