Multivariate Adaptive Regression Splines Approach Research Articles

The Role of Information and Communication Technologies and Energy‐Related Research and Development Investments in Energy Transition: Evidence from the United States of America by Machine Learning Algorithm

In this study, given the critical role and importance of the energy transition, the effect of information and communication technologies (ICT) is researched and the levels of energy‐related research and development (R&D) investments on the energy transition index (ETI), controlling for human capital (HC), energy consumption (EC), and income (gross domestic product [GDP]) are disaggregated. Therefore, in this study, the United States of America (USA) is focused on as the world's leading economy, a total of five machine learning (ML) algorithms are performed, and the data from 2000/Q1 to 2021/Q4 are used. In the outcomes, it is shown that: 1) the multivariate adaptive regression splines approach is the best estimation algorithm among the ML approaches based on the coefficient of determination (R2), which has 85% estimation capacity for ETI; 2) ICT and EC are the most important factors for ETI, followed by renewable energy R&D investments, energy efficiency R&D investments, HC, GDP, and nuclear energy R&D investments, in that order; and 3) R&D investments in carbon capture and storage has no significant effect on ETI. In the overall results of the study, it is suggested that technological progress is central to energy transition in the USA and that environmental policies implemented for energy transition should be closely linked to technological progress.

Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

The effect of electricity consumption determinants in household load forecasting models

Usually, household electricity consumption fluctuates, often driven by several electrical consumption determinants such as income, household size, and price. Recently, research studies on the investigation of predictor variables in household electricity consumption have increased especially in the developing and newly industrialized countries. However, the studies just focus on identifying the predictor variables of household electricity consumption that influence load forecasting models. In Tanzania, for instance, scholars found that using the “income” determinant improves the performance of a forecasting model. The scholars suggest without any empirical bases that adding more predictor variables would have improved the accuracy of the model. This study aims to analyze the effect of the number of predictor variables on household load forecasting performance based on Tanzania’s data. Nonlinear regression based on a Weibull function and multivariate adaptive regression splines approaches are used for this purpose. Our findings indicate that income, household size, and number of appliances are common predictor variables of household consumption in developing countries. The measured forecasting root-mean-square error (RMSE) when using income, household size, and the number of appliances is 0.8244, 1.2314, and 0.9868, respectively. Finally, we forecasted load using all three determinants and the RMSE dropped to 0.7031. Having obtained the smaller value of RMSE when all predictors are used reveals that the inclusion of all three predictor variables in load forecasting leads to a significant decrease in RMSE by 14.73%. Therefore, the study recommends using multiple predictor variables in load forecasting models to increase accuracy.

Developing a used car pricing model applying Multivariate Adaptive regression Splines approach

Although the used car market in India is enormous, with an annual 27.1 billion USD worth of car sales, no academic study examining the pricing of Indian used cars is available. The average on-road life of cars in India is high, exceeding twenty years and pass through multiple hands. The price of old cars is also a primary determinant of the prices of new cars. The article discusses the non-linear reduction of used car prices on account of age, kilometer driven, and the number of previous owners of the cars. In addition to using the Ordinary Linear Squares (OLS), we also applied Multivariate Adaptive Regression Splines (MARS) techniques to capture non-linear price relationships with other depreciating variables. The resulting error estimation from these two methods demonstrates that the complexity of non-linear modeling using MARS significantly improves fitment accuracy.

The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach

This study explores the non-linear relationship between air pollution, socio-economic factors, labor insurance, and labor productivity in the industrial sector in Taiwan. Using machine learning, specifically multivariate adaptive regression splines (MARS), provides an alternative approach to examining the impact of air pollution on labor productivity, apart from the traditional linear relationships and parametric methods employed in previous studies. Examining this topic is imperative for advancing the knowledge on the effects of air pollution on labor productivity and its association with labor insurance, employing a machine learning framework. The results reveal that air pollution, particularly PM10, has a negative impact on labor productivity. Lowering the PM10 level below 36.2 μg/m3 leads to an increase in marginal labor productivity. Additionally, the study identifies labor insurance as a significant factor in improving productivity, with a 9% increase in the total number of labor insurance holders resulting in a substantial 42.9% increase in productivity. Notably, a link between air pollution and insurance is observed, indicating that lower air pollution levels tend to be associated with higher labor insurance coverage. This research holds valuable implications for policymakers, businesses, and industries as it offers insights into improving labor productivity and promoting sustainable economic development.

Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters

Recently, biomass has become an increasingly widely used energy resource. The problem with the use of biomass is its variable composition. The most important property that determines the energy content and thus the performance of fuels such as biomass is the heating value (HHV). This paper focuses on selecting the optimal number of input variables using linear regression (LR) and the multivariate adaptive regression splines approach (MARS) to create an artificial neural network model for predicting the heating value of selected biomass. The MARS model selected the input data better than the LR model. The best modeling results were obtained for a network with three input neurons and nine neurons in the hidden layer. This was confirmed by a high correlation coefficient of 0.98. The obtained results show that artificial neural network (ANN) models are effective in predicting the calorific value of woody and field biomass, and can be considered a worthy simulation model for use in selecting biomass feedstocks and their blends for renewable fuel applications.

Non-parametric, semi-parametric, and machine learning models for river temperature frequency analysis at ungauged basins

River water temperature is essential in regulating many physical and biochemical processes in river systems. Consequently, it is crucial to develop reliable tools for predicting extreme river temperatures at sites with little or no available data. This study aims to compare two machine learning models, random forest (RF) and extreme gradient boosting (XGBoost), with non-parametric multivariate adaptive regression splines (MARS) and semi-parametric generalized additive models (GAMs) for the regional estimation of maximum water temperatures at ungauged locations. Three linear and non-linear approaches are also considered in the homogeneous regions delineation step of regional frequency analysis: canonical correlation analysis (CCA), neural network-based canonical correlation analysis (NLCCA), as well as considering all stations (ALL). The results indicate that GAM and MARS lead to the best performances. The performance of NLCCA+GAM is the best in terms of absolute and relative mean square error, followed by CCA + MARS. A significant improvement in the performance of adopted models is achieved by using neighborhood methods. The two machine learning models are tested using two variable selection methods: Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). The results, however, do not show any significant differences. These results may be indicative of the flexibility and ability of the GAM and MARS approaches to reproduce thermal extremes, especially under real-world conditions when a limited amount of data is available.

Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform

Accurate daily solar radiation prediction is a crucial task for the management and generation of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy of new machine learning methods, wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron artificial neural network (WMLPANN), long short-term memory (LSTM), multi-layer perceptron artificial neural network (MLPANN), and multivariate adaptive regression splines (MARS), was assessed for modeling daily solar radiation using various input combinations of climatic data of maximum and minimum relative humidity, potential evapotranspiration, maximum and minimum temperature, precipitation and wind speed from two stations, Brownstown and Carbondale located in Illinois, USA. For accurate assessment of prediction accuracy of the proposed models, four reliable statistical metrics, root mean squared error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and One-Tailed Wilcoxon Signed-Rank Test were employed. Comparison of results, based on the RMSE values, indicated that the WLSTM method performed better than the WMLPANN, LSTM, MLPANN and MARS methods in the estimation of solar radiation values at both stations. The average RMSE values of WMLPANN, LSTM, MLPANN, and MARS approaches was decreased by 6%, 4%, 7.3%, and 13.5% using WLSTM method at Brownstown Station, by 6%, 5.2%, 13.2%, and 14.3% at Carbondale Station, respectively. The overall results during the testing phase of both stations revealed the successful application of hybridization of the LSTM model with the wavelet transform technique for improving the prediction accuracy of solar radiation based on climatic parameters.

Yaşam Süresi Verilerinin Çok Değişkenli Uyumlu Regresyon Analizinde Kullanımı: Simülasyon Çalışması

Objective: The multivariate adaptive regression splines (MARS) is very effective in order to model linear or nonlinear relationships. The Cox regression residuals-based MARS model, which integrates Cox regression and MARS approaches, was created to assess the relationships between efficient risk factors on the survival. The purpose of this study is to introduce the Survival-MARS (SM) model which uses the Cox-Snell, Martingale, and deviance residuals. Also,our aim is to compare the performance of the models created with residuals at different sample sizes and correlation levels with the simulation study in order to determine the most effective residual type that can be used in the SM model. Material and Methods: Performances of SM models that use CoxSnell,Martingale, and deviance residual types were compared at different sample sizes (n = 30, 100, 150, 250, 500, 1.000), with both no correlation (r = 0.00) and medium (r = 0.50) and high (r = 0.90) correlations between predictors. SM model performances were compared via minimum generalized cross-validation and the sum of mean squared error values.. Results: In all scenarios, SM models with Cox-Snell residuals have the best performance compared to other models established with other residuals. Martingale and deviance residuals were affected by high correlation and low sample sizes. Conclusion: In case of linear relationship between risk factors, SM models with Cox-Snell residuals are quite successful in explaining these relationship structures and enable the effects on the dependent variable to easily interpret.

A comparative analysis of biomass torrefaction severity index prediction from machine learning

Machine learning (ML) is one type of artificial intelligence (AI) commonly used for computer programming. Multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) are two common and popular tools in AI that allow the user to analyze the pattern of complex data. The torrefaction severity index (TSI) is an index to define torrefied biomass quality at different torrefaction conditions. In this study, MARS and ANN models are applied to predict TSI. The considered input parameters in predictions using MARS and ANN approaches comprise feedstock type, temperature, and duration. The MARS model indicates that temperature is the most influential factor on TSI, followed by duration and feedstock type. In contrast, the ANN model reveals that the feedstock type is a dominant factor, and temperature and duration are not important. The performance of the ANN model is evaluated in three different combinations of numbers of hidden layers and neurons. It shows 2 hidden layers along with 85 neurons giving the best performance. The highest R2 values in MARS and ANN are 0.9851 and 0.9784, respectively. The relative root means square error analysis shows that both MARS and ANN have good fit quality with the relative errors of 1.49% and 2.16%, respectively. Overall, the comparison reflects that MARS is a more suitable model for predicting solid biofuel’s TSI. The general observation suggests that the ANN lacks sensitivity to the input parameter. Nevertheless, ANN performance may be improved by adjusting the number of hidden layers and neurons.

ANALISIS SURVIVAL PENDERITA GAGAL GINJAL DENGAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE

Survival analysis is a statistical method where the outcome variable that is considered is the time until the occurrence of an event or called an event. Survival analysis in the medical world can be used to test the survival of patients suffering from a disease, one of which is kidney failure. Based on the results of the 2013 Basic Health Research, 0.2% of the total population of Indonesia, namely 499,800 people, experienced kidney failure. Kidney failure is a process of decreasing kidney function that requires renal replacement therapy, in the form of hemodialysis or kidney transplantation. If the survival data involve independent variables that are thought to affect the survival time, then the Cox Proportional Hazard (PH) regression approach can be developed which can be developed for the Multivariate Adaptive Regression Spline (MARS) approach. The MARS approach is a nonparametric regression approach with high-dimensional data, namely data with an independent variable of 3≤𝑝≤20 and a data sample of 50≤N≤1000. This study aims to describe the characteristics of patients with kidney failure, identify the factors that affect the survival time of patients with kidney failure and determine the MARS model. The MARS approach is obtained from a combination of functional basis, maximum interaction, and minimum observation by trial and error. Based on the model that has been formed using the MARS approach, it is found that the most influential variables on the survival time of patients with kidney failure are age, urea levels, and creatine levels.
 Key words: Renal failure, hemodialysis, survival analysis, nonparametric, MARS
  

A novel artificial intelligence approach for regolith geochemical grade prediction using multivariate adaptive regression splines

• Multivariate Adaptive Regression Spline (MARS) is proposed. • The proposed MARS approach was used to predict regolith geochemical grade. • Three other artificial intelligence techniques and kriging were compared with MARS. • The MARS technique gave superior results. The necessity for applying a potent analytical regolith geochemical grade estimator is driven by the reality of mineral exploration. This is because many exploration geologists rely upon the classical geostatistical method of Kriging which oftentimes do not produce accurate predictions due to the complexity of interactions between geological features and spatial variables. In this study, a novel non-linear data-driven approach known as Multivariate Adaptive Regression Spline (MARS) is proposed as an effective predictive tool to unravel regolith geochemical complexities. The proposed MARS approach was used to predict regolith geochemical grade from a thick regolith cover in the Tarkwaian paleo-placer of the South-Western Ashanti belt in Ghana. Out of the 891 samples, the data was partitioned into 70% training (model development) and 30% testing (model validation). The proposed MARS result was compared with three other artificial intelligence techniques (i.e., radial basis function neural network, backpropagation neural network and generalised regression neural network) and kriging geostatistical technique. Based on the test results, MARS had the highest correlation coefficient ( R = 0.9675) and the least statistical error metrics (RMSE = 0.7791, MAE = 0.6014, and ρ = 0.0472). The implementation of the MARS approach in regolith geochemical grade estimation domain has yielded outstanding and promising results. The MARS superiority was evident in its calibration strength, prediction accuracy, robust interaction of variables and overcoming the black box system of ANN. Thus, the proposed MARS approach could be an excellent tool in regolith geochemical grade estimation workflow when fully integrated with exploration tasks.

Flood frequency analysis at ungauged catchments with the GAM and MARS approaches in the Montreal region, Canada

Regional frequency analysis (RFA) aims to estimate quantiles of extreme hydrological variables (e.g. floods or low-flows) at sites where little or no hydrological data is available. This information is of interest for the optimal planning and management of water resources. A number of regional estimation models are evaluated and compared in this study and then used for regional estimation of flood quantiles at ungauged catchments located in the Montreal region in southern Quebec, Canada. In this study, two neighborhood approaches using canonical correlation analysis (CCA) and the region of influence (ROI) method are applied to delineate homogenous regions. Three regression methods namely log-linear regression model (LLRM), generalized additive models (GAM), and multivariate adaptive regression splines (MARS), recently introduced in the RFA context, are considered for regional estimation. These models are also applied considering all stations (ALL). The considered models, especially MARS, have never been used previously in a concrete application. Results indicate that MARS and GAM have comparable predictive performances, especially when applied with the whole dataset. Results also show that MARS used in combination with the CCA approach provide improved performances compared to all considered regional approaches. This may reflect the flexibility of the combination of these two approaches, their robustness, and their ability to better reproduce the hydrological phenomena, especially in real-world conditions when limited data are available.

Development of Nonparametric Path Model using Multivariate Adaptive Regression Spline (MARS) Method In Compliance Behavior of Paying Credit In Bank

The purpose of this study is to develop a Non-parametric Path with the MARS (Multivariate Adaptive Regression Spline) approach which is applied to the behavior of paying credit compliance at Bank. prospective debtor by a Bank. The data used in this study is primary data using a research instrument in the form of a questionnaire. There are 7 variables, namely 5 exogenous variables in the form of 5C variables (Character (X1), Capacity (X2), Capital (X3), Collateral (X4), Condition of Economy (X5)), and two endogenous variables, namely Punctual Payment (Y1), Obedient Paying Behavior (Y2). Variable measurement technique is done by calculating the average score on the items. Sampling in this study used a purposive sampling technique with the criteria of respondents in the study were mortgage debtors (House Ownership Credit) at Bank X. Respondents obtained in this study were 100 respondents. The analysis used is nonparametric path with Multivariate Adaptive Regression Spline (MARS) approach. The result of this research is the estimation of nonparametric Path function using MARS approach on various interactions. The best estimate of the function of obedient behavior in paying credit is when it involves 4 variables, namely Character (X1), Capacity (X2), Conditions of economy (X5), and On time pay (Y1) with a value of generalized cross-validation The smallest (GCV) obtained is 0.2496. The originality of this research is the development of a nonparametric path with the MARS approach that is able to capture interactions between existing variables and is also able to handle the limitations of the truncated spline to determine the position and number of knot points used when involving many predictor variables. There has been no previous research that has examined the development of a nonparametric path with the MARS approach.

Probabilistic Oblique Impact Analysis of Functionally Graded Plates – A Multivariate Adaptive Regression Splines Approach

Purpose: To investigate the probabilistic low-velocity impact of functionally graded (FG) plate using the MARS model, considering uncertain system parameters. Design/methodology/application: The distribution of various material properties throughout FG plate thickness is calculated using power law. For finite element (FE) formulation, isoparametric elements with eight nodes are considered, each component has five degrees of freedom. The combined effect of variability in material properties such as elastic modulus, modulus of rigidity, Poisson’s ratio, and mass density are considered. The surrogate model is validated with the FE model represented by the scatter plot and the probability density function (PDF) plot based on Monte Carlo simulation (MCS). Findings: The outcome of the degree of stochasticity, impact angle, impactor’s velocity, impactor’s mass density, and point of impact on the maximum value of contact force (CFmax ), plate deformation (PDmax), and impactor deformation (IDmax ) are determined. A convergence study is also performed to determine the optimal number of the constructed MARS model’s sample size. Originality/value: The results illustrate the significant effects of uncertain input parameters on FGM plates’ low-velocity impact responses by employing a surrogate-based MARS model.

High-dimensional black-box optimization under uncertainty

Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research—unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first design a new flexible non-interpolating parsimonious surrogate model using a partitioning-based multivariate adaptive regression splines approach, Tree Knot MARS (TK-MARS). The proposed model is specifically designed for optimization by capturing the structure of the function, bending at near-optimal locations, and is capable of screening unimportant input variables. Furthermore, we develop a novel replication approach called Smart-Replication, to overcome the uncertainty associated with the black-box output. The Smart-Replication approach identifies promising input points to replicate and avoids unnecessary evaluations of other data points. Smart-Replication is agnostic to the choice of a surrogate and can adapt itself to an unknown noise level. Finally to demonstrate the effectiveness of our proposed approaches we consider different complex global optimization test functions from the surrogate optimization literature. The results indicate that TK-MARS outperforms original MARS within a surrogate optimization algorithm and successfully detects important variables. The results also show that although non-interpolating surrogates can mitigate uncertainty, replication is still beneficial for optimizing highly complex black-box functions. The robustness and the quality of the final optimum solution found through Smart-Replication are competitive with that using no replications in environments with low levels of noise and using a fixed number of replications in highly noisy environments.

Prediction on the volume of non‐performing loans in Turkey using multivariate adaptive regression splines approach

AbstractAs the volume of non‐performing loans (NPL) plays an important role in the capacity of providing loans, increasing NPL problem is perceived as one of the major challenges in a bank‐based financial system. In recent years, Turkey has been confronted with this problem. In order to reduce NPL volume, necessary precautions that can contribute to government administrators should be taken. Therefore, the most important predictors on NPL volume were determined by using predictive analytics such as multivariate adaptive regression splines that is found out the optimal parameter effects from high‐dimensional data. The dataset, which is obtained from the Central Bank of the Republic of Turkey and Banking and Regulation Supervision Agency, consists of 14 independent variables from 2005 and 2019. As a result of this study, it is indicated that credits, USD/TL exchange rate, and the unemployment rate are the most statistically significant factors in defining the level of NPL volume.

Multivariate Adaptive Regression Spline approach for the assessment of surface mean pressure coefficient on surfaces of C-shaped building

Proper assessment of wind load enables durable design of structures under varying wind load conditions. The accurate prediction of pressure coefficient on any irregular plan shaped buildings is essential for the assessment of wind loads and the structural design. The main objective of this study is to present an equation in the line of Multivariate Adaptive Regression Spline (MARS) approach using experimental data of surface mean pressure coefficient. This developed equation can be used satisfactorily for the prediction of pressure coefficient values accurately on the surfaces of C-shaped buildings. An extensive experimentation was carried out to obtain coefficient of pressure over the surfaces of C-shaped models under varying sizes, corner curvature and angle of incidence in a sub-sonic wind tunnel. The predicted values of pressure coefficient of different C-shaped buildings using developed model are compared with equations developed by Swami and Chandra’s (S&C) and Muehleisen and Patrizi’s (M&P). The comparison indicates that the proposed MARS model predicts pressure coefficient values more accurately than those by S&C and M&P models on frontal as well as side surfaces. Further, the model is used to validate with the actual building, Tokyo Polytechnic University (TPU) data to show the applicability of the proposed equation.

Analysis of Access to Financial Services on Poverty Alleviation with MARS Approach

This study aimed to determine the barriers of public access to financial services and their effects on poverty alleviation. The sample used was 6 ASEAN countries (Indonesia, Singapore, Malaysia, Vietnam, Thailand, and the Philippines) from 2006 to 2015. The analytical method used was the MARS. MARS is one of the nonparametric regression methods as an alternative to the multiple linear regression method, which must fulfill parametric assumptions. The results of the study using MARS show that the model formed has a high coefficient of determination, and criteria of the test of the suitability of the model are met. In other words, multivariate adaptive regression spline (MARS) can explain well the variability of the independent variables on the dependent variable. The results of the hypothesis testing using the MARS method show that indicators of macroeconomic, social, bank characteristics, institutions, and regulations affect access to financial services (AFS) and AFS affect poverty alleviation. This finding shows that increasing AFS will affect poverty reduction, and to increase public AFS can be done by minimizing macroeconomic, regulatory, social, bank, and institutional constraints.

Prediction of load-displacement performance of grouted anchors in weathered granites using FastICA-MARS as a novel model

With the rising needs of better prediction of the load-displacement performance of grouted anchors in an era of developing large-scale underground infrastructures, the existing methods in literature lack an accurate analytical model for the real-life projects or rigorous understanding of the parameters such as grouting pressures. This paper proposes FastICA-MARS as a novel data-driven approach for the prediction of the load-displacement performance of uplift-resisting grouted anchors. The hybrid and data-driven FastICA-MARS approach integrates the multivariate adaptive regression splines (MARS) technique with the FastICA algorithm which is for Independent Component Analysis (ICA). A database of 4315 observations for 479 different anchors from 7 different projects is established. The database is then used to train, validate and compare the FastICA-MARS approach with the classical MARS approach. The developed FastICA-MARS model can provide more accurate predictions than MARS. Moreover, the developed FastICA-MARS model is easy to interpret since the evaluation of the parameter importance of the independent components can be conducted along with the considerations of the correlations with the original variables. It is noteworthy to point out that the grouting pressures play a central role in the proposed model, which is considered of paramount importance in engineering practices but has not been properly taken into account in any prior analytical or empirical predictive models for the load-displacement relationships.