## What is/are Multivariate Adaptive?

Multivariate Adaptive - In the article, we present a modern approach to knowledge extraction based on Artificial Intelligence (AI) and Multivariate Adaptive Regression Splines optimizing the availability of HR with a high innovation rate, taking into account their availability time and location.^{[1]}A hybrid system based on one of the evolution algorithm – Genetic Algorithm (GA), fused with a well-known data-driven model of multivariate adaptive regression splines (MARS), namely G-MARS, was proposed and applied.

^{[2]}This study was also conceived to address and investigate the efficiency of EANN for forecasting monthly surface runoff and compare the performances with conventional feed forward neural network (FFNN) and multivariate adaptive regression spline (MARS) models.

^{[3]}Three machine learning approaches, including Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression, were used.

^{[4]}Multivariate Adaptive Regression Spline (MARS) is a nonparametric regression approach that results from a spline with Recursive Partitioning Regression (RPR) which was first popularized by Friedman (1991).

^{[5]}To better understand the impacts of climate change on the hydrological cycle, long-term simulations of multiple earth system models from the Coupled Model Intercomparison Project (CMIP Phase 5) are statistically downscaled and bias-corrected using Multivariate Adaptive Constructed Analogs (MACA) scheme for use as model forcing.

^{[6]}The kinematic results from this simulation were used in a Multivariate Adaptive Regression Spline algorithm, predicting attainment of a Patient Acceptable Symptom State (PASS) score in captured 12 month post-operative Knee Injury and Osteoarthritis Outcome Scores (KOOS).

^{[7]}We analyzed the performance of two nonparametric models (the CART decision tree, CDT, and multivariate adaptive regression curves, MARS) in the páramos of the Chambo sub-basin (Ecuador).

^{[8]}To do so, different data mining methods including k-means clustering, Decision Trees (DT), Multivariate Adaptive Regression Splines (MARS), and Generalized Regression Neural Networks (GRNNs) are employed.

^{[9]}To model the trajectory of the pandemic in Kuwait from February 24, 2020 to February 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), and then mapped the key breakpoints of the models to the set of government-enforced interventions.

^{[10]}This study combines the proposed partial least squares–based multivariate adaptive regression spline (PLS–MARS) method and a regional multi-variable associate rule mining and Rank–Kennard-Stone method (MVARC-R-KS) to construct a nonlinear prediction model to realize local optimality considering spatial heterogeneity.

^{[11]}The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modeling error of about 5% compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines.

^{[12]}We have employed three methods—ordinary least square, multivariate adaptive regression splines (MARS) and autoregressive integrated moving average (ARIMA-X) to find the predictive capacity of these above-mentioned variables.

^{[13]}To enable use of the model when such information is not available, the relationship between the values of the site-specific parameter and environmental variables was described using Multivariate Adaptive Regression Splines (MARS).

^{[14]}This presentation aims to represent the latest findings of our efforts in developing an “alternative” H35 FSC product for the mountainous part by using two data-driven machine learning methodologies, namely, multivariate adaptive regression splines (MARS) and random forests (RFs).

^{[15]}Before predicting geosmin concentration, we categorized it into four groups based on the boxplot method, and multivariate adaptive regression splines, classification and regression trees, and random forest (RF) were applied to identify the most appropriate modelling to predict geosmin concentration.

^{[16]}The purpose of this study is to predict the parameters such as fan angle, air velocity， and tunnel length, which are used in the design of pneumatic cleaners, through the multivariate adaptive regression splines (MARS) method.

^{[17]}The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM).

^{[18]}A machine learning model, Multivariate Adaptive Regression Spline (MARS), was utilized to determine the relative importance of climatic, topographic, forest structure and human modification variables on fire dynamics across wet and dry seasons, both in El Niño and non-El Niño years.

^{[19]}So that, in this research used nonparametric regression approach which multivariate adaptive regression splines (MARS) for modeling the level open unemployment in Central Java at 2014 because the level open unemployment in Central Java predicted influence by some factors.

^{[20]}Additionally, two machine learning methods, including the back propagation neural network (BPNN) and multivariate adaptive regression splines (MARS), were used to compare with the proposed method.

^{[21]}This paper aims to accurately estimate the bond strength of spliced GFRP bars in concrete beams by applying three soft computing models including multivariate adaptive regression spline (MARS), Kriging, and M5 model tree.

^{[22]}Climatic projections for three climate models ranging from the driest to wettest conditions were obtained from the Multivariate Adaptive Constructed Analogs (MACA) dataset.

^{[23]}Three global sensitivity analysis (SA) methods, namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model.

^{[24]}Afterward, the topographic attributes were used for multivariate adaptive regression splines (MARS) modeling of solum thickness.

^{[25]}Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows.

^{[26]}Multivariate Adaptive Regression Spline (MARS) is one of the non-parametric regression approaches.

^{[27]}The multivariate adaptive regression splines (MARS) model is a flexible non-parametric sparse regression algorithm and provides an excellent promise to data fitting through nonlinear basis functions.

^{[28]}We then fit a multivariate adaptive regression spline model for the probability of road obstruction as a function of road segment length and landslide hazard, using a training and validation dataset derived from the intersections of road networks with earthquake-triggered landslide inventories.

^{[29]}MATERIAL AND METHODS Implementation of the multivariate adaptive regression spline (MARS) method in relation to IMR between 1980 and 2016, and search for related articles published between 1980 and 2019 in SciELO, Lilacs, PubMed, Cochrane Library, and Embase.

^{[30]}This paper applies the Multivariate Adaptive Constructed Analogs (MACA) statistical downscaling method to 12 general circulation models to produce 21st century projections of fire weather variables over Victoria, Australia, under two emissions scenarios.

^{[31]}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).

^{[32]}We propose a novel nonparametric regression framework based on multivariate adaptive regression splines (MARS) for ECGI.

^{[33]}This paper aims to propose a sliced inverse regression (SIR)-based multivariate adaptive regression spline (MARS) method for slope reliability analysis in spatially variable soils, which combines the advantages of both SIR and MARS.

^{[34]}Multivariate Adaptive Regression Spline (MARS) is a nonparametric regression method that can accommodate additive effects and interaction effects between predictor variables.

^{[35]}The coupled SWAT-MODFLOW modeling code is used as the hydrologic simulator and forced with five different CMIP5 climate models downscaled by Multivariate Adaptive Constructed Analogs (MACA), each for two climate scenarios, RCP4.

^{[36]}To dissect the synergistic interactions between exogenous probiotics and the autochthonous microbiota a multivariate adaptive regression splines (MARS) model was implemented towards the development of optimized probiotic combinations with therapeutic benefits.

^{[37]}The multivariate adaptive regression splines algorithm is subsequently applied to develop a predictive model for the normalized heave of a specimen.

^{[38]}We compare NNRTs, ORT-LH, Multivariate Adaptive Regression Splines (MARS), Random Forests (RF) and XGBoost on 40 real-world datasets and show that overall NNRTs have a performance edge over all other methods.

^{[39]}The main objective of this research is to predict transport energy demand using Multivariate Adaptive Regression Splines (MARS) as a nonparametric regression technique.

^{[40]}The Multivariate Adaptive Regression Splines (MARS) approach is a multivariate nonparametric regression analysis that assumes the form of a functional relationship between response variable and predictors whose patterns are unknown.

^{[41]}This study used a quantitative method using Multivariate Adaptive Regression Splines (MARS).

^{[42]}We introduce a multivariate adaptive sampling algorithm for the passivity characterization of parameterized macromodels.

^{[43]}Although this issue has been thoroughly studied by many institutional researchers using parametric tech- niques, such as regression analysis and logit modeling, this article attempts to bring in a new perspective by exploring the issue with the use of three data mining techniques, namely, classification trees, multivariate adaptive regression splines (MARS), and neural networks.

^{[44]}This study aimed to evaluate the performance of multivariate adaptive regression splines (MARS) and extremely randomized trees (ERT) models for predicting the internal and external dust events frequencies (DEF) across the northeastern and southwestern regions of the Gavkhouni International Wetland.

^{[45]}In this study, a multivariate adaptive regression splines (MARS) model and a novel deep forest algorithm were applied to predict and classify rockburst intensity of a database including 344 rockburst cases collected worldwide.

^{[46]}Multivariate Adaptive Regression Splines (MARS) used to model the active student’s status in the Department of Statistics at Universitas Terbuka and determine the factors that influence the response variable.

^{[47]}Hydroclimatic projections were simulated using the Variable Infiltration Capacity (VIC) model driven by three downscaled climate models from the Multivariate Adaptive Constructed Analogs (MACA) dataset to cover driest to wettest future conditions in the conterminous United States (CONUS).

^{[48]}We also describe the relationship between the MGR estimator which is not sparse and a multivariate adaptive group Lasso estimator which is sparse, under orthogonal explanatory variables.

^{[49]}In this paper, we assess the ability of the Multivariate Adaptive Constructed Analogs (MACA) method to downscale output from general circulation models over Victoria, and replicate statistical attributes of fire danger indices.

^{[50]}

## artificial neural network

, artificial neural networks (ANN) and multivariate adaptive regression splines (MARS)) were developed using cumulative percentiles from grain-.^{[1]}Four regression methods have been used: linear multiple regression, artificial neural networks, random forests, and multivariate adaptive regression splines.

^{[2]}The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR).

^{[3]}The artificial neural networks, Gaussian process regression, support vector machines and multivariate adaptive regression splines were employed as stand-alone machine learning models, while the discrete wavelet transform was utilized as a pre-processing technique to create hybrid models.

^{[4]}Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour concentrations using an advanced IOMS.

^{[5]}In order to find a proper model, three common machine learning (ML) techniques including artificial neural network (ANN), random forest (RF), and multivariate adaptive regression splines (MARS) were used.

^{[6]}The data obtained through laboratory experiments was then employed to model the dynamic response of MSW using four different machine learning techniques including Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), Multi-Gene Genetic Programming (MGGP), and M5 model Tree (M5Tree).

^{[7]}In the present work, for the first time, free vibration response of angle ply laminates with uncertainties is attempted using Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Gaussian Process Regression (GPR), and Adaptive Network Fuzzy Inference System (ANFIS).

^{[8]}, artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), Naive Bayesian classifier (NBC), K-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting (AdaBoost) and maximum entropy (MaxEnt), we propose a weighted approach to characterize the forest fire susceptibility of the region using the outputs of the different MLTs.

^{[9]}To this end, the pier scour depth was estimated in non-cohesive soils based on a subcritical flow and live bed conditions using the artificial neural networks (ANN), group method of data handling (GMDH), multivariate adaptive regression splines (MARS) and Gaussian process models (Kriging).

^{[10]}In this study, six habitat models were compared to quantify the spatio-temporal distribution of small yellow croaker (Larimichthys polyactis) in Haizhou Bay, including Generalized Additive Model (GAM), K-Nearest Neighbor (KNN), Multivariate Adaptive Regression Splines (MARS), Generalized Boosted regression Model (GBM), Random forest (RF) and Artificial neural network (NN).

^{[11]}Multivariate adaptive regression splines (MARS) and artificial neural network (ANN) algorithms have been implemented to build the prediction model.

^{[12]}Thus, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM).

^{[13]}Thus, this study evaluates artificial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ETo using temperature.

^{[14]}In this research, based on experimental studies of large hydraulic models, five well-known soft computing methods including artificial neural networks (ANN), gene expression programming (GEP), classification and regression trees (CART), M5 model tree (M5MT), and multivariate adaptive regression splines (MARS) approaches are examined.

^{[15]}Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models.

^{[16]}The results show that the proposed retrieval models based on random forest regression technique perform much better using independent test data for all land cover classes, with higher accuracy and no out-of-range estimated values, when compared to the other three approaches (linear regression, artificial neural networks (ANN), and multivariate adaptive regression splines (MARS)).

^{[17]}Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS).

^{[18]}Here, we used multiple linear mixed models (LMM), generalized additive models (GAM), multivariate adaptive regression splines (MARS), and artificial neural networks (ANN) to model species richness and diversity of freshwater fishes in eastern and central India.

^{[19]}In this paper, we suggest three models based on multivariate adaptive regression splines (MARS), artificial neural network (ANN) and linear regression (LR) methods to model electrical load overall in the Turkish electricity distribution network, and this not only by long-term but also mid- and short-term load forecasting.

^{[20]}In this study, at first, the capabilities of two time series analysis approaches, namely self-exciting threshold autoregressive (SETAR) and generalized autoregressive conditional heteroscedasticity (GARCH) models, then three artificial intelligence approaches including artificial neural networks (ANN), multivariate adaptive regression splines (MARS), and random forests (RF) models were investigated to predict monthly river flow.

^{[21]}

## support vector machine

Here, geographic information systems (GIS) and three machine learning techniques (MARS-multivariate adaptive regression splines, RF-random forest and SVM-support vector machine) were used, for the first time, to predict sediment deposition rate (SR) in check-dams located in six watersheds in SW Spain.^{[1]}We fitted three different machine learning algorithms, multivariate adaptive regression splines, random forest and support vector machines utilizing 36 candidate predictors.

^{[2]}These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest.

^{[3]}The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Naive Bayes (NB), for landslide susceptibility modeling and comparison of their performances.

^{[4]}In this report, a few of artificial intelligence algorithms such as support vector machine, boosted decision tree regression, random forest and multivariate adaptive regression spline will be used in the development of best model algorithm in earthquake prediction.

^{[5]}Thus, the present study aims to investigate the applicability of five surrogate models such as moving least square, support vector machine, radial basis function, polynomial neural network and multivariate adaptive regression splines in terms of their efficiency and accuracy.

^{[6]}In the present study, four artificial intelligence techniques namely, multivariate adaptive regression splines (MARS), partial least squares regression (PLSR), K nearest neighbor (KNN) and support vector machine (SVM) were utilized to develop prediction models to estimate reliable $${M}_{R}$$ of unbound granular material and their performances were compared with one other.

^{[7]}This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay.

^{[8]}These predictions were made using machine learning techniques such as LASSO (Least Absolute Shrinkage and Selection Operator) penalized regression and elastic-net, as well as algorithms like k-nearest neighbors (KNN), MARS (Multivariate Adaptive Regression Splines), random forest, boosted trees and SVM (Support Vector Machine).

^{[9]}The gathered data facilitated to calibrate the physical model of Deardorff and establish parameters of: support vector machines, multivariate adaptive regression spline, and boosted trees model.

^{[10]}The main aim was to evaluate the support vector machine (SVM), conditional inference random forest (CRF), and stochastic gradient boosting (SGB) models based on three FS algorithms, including Boruta, multivariate adaptive regression splines (MARS), and recursive feature elimination (RFE) techniques in predicting NDDs around the Hamoun wetlands.

^{[11]}We built these models using the machine learning algorithms Support Vector Machine (SVM), Generalized Linear Model (GLM), Multivariate Adaptive Regression Spline (Mars), Random Forest (RF), XGBoost, Ridge Regression (Ridge), and Cubist based on UAV hyperspectral data and heavy metal field fast detection data in typical potentially contaminated sites.

^{[12]}To identify the best machine learning algorithm, random-forest (rf), support vector machines (svm), multivariate adaptive regression spline (mars) extreme gradient boosting (xgb) and adaptive boosting (adaboost) were used on the training and test data to identify the best performing algorithm.

^{[13]}This research evaluates an As-susceptible region, the Tajan River watershed, using the following data-mining models: multivariate adaptive regression splines (MARS), functional data analysis (FDA), support vector machine (SVM), generalized linear model (GLM), multivariate discriminant analysis (MDA), and gradient boosting machine (GBM).

^{[14]}Seven machine learning algorithms are created to compare predictive thermal performance of this evaporator, including linear model, Lasso (least absolute shrinkage and selection operator), MARS (multivariate adaptive regression splines), NNet (averaging neural network), CB (Cubist model), GP (Gaussian process), and SVM (support vector machine).

^{[15]}

## gene expression programming

The present study focuses on the prediction of rainfall using the most advanced soft computing techniques (SCT) such as multivariate adaptive regression splines (MARS), classification and regression trees (CART), and gene expression programming (GEP) in India’s Udaipur district.^{[1]}After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation.

^{[2]}Hence, four well-known DDMs such as Evolutionary Polynomial Regression (EPR), M5 Model Tree (MT), Gene-Expression Programming (GEP), and Multivariate Adaptive Regression Spline (MARS) are employed to predict WQI in Karun River.

^{[3]}Performance of DDTs indicated that the Evolutionary Polynomial Regression (EPR) demonstrated the most accurate predictions of GQI than a model tree (MT), gene-expression programming (GEP), and Multivariate Adaptive Regression Spline (MARS).

^{[4]}In this paper, various artificial intelligence approaches such as multivariate adaptive regression spline (MARS), group method of data handling Neural Network (GMDH-NN), and gene-expression programming (GEP) are adopted to construct model equations for determining %Sfp for meandering compound channels with relative roughness.

^{[5]}We introduce a method based on thorough analysis of different calibration datasets, resampled from a global database of tracer studies, to determine the uncertainty associated with five applicable intelligent models for estimation of Kx and Ky (model tree, evolutionary polynomial regression (EPR), gene-expression programming, multivariate adaptive regression splines (MARS), and support vector machine (SVM)).

^{[6]}In this study, values of SPEI are formulated for various climates by three robust Artificial Intelligence (AI) models: Gene Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS).

^{[7]}After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation.

^{[8]}Gene expression programming (GEP), multivariate adaptive regression spline (MARS), and random forest (RF) models were employed.

^{[9]}

## extreme gradient boosting

The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods.^{[1]}The Lasso-logistic regression, multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost) algorithms were used to construct risk prediction models in the training set, and model performance was verified in the validation set.

^{[2]}The current study introduced three versions of newly explored ensemble machine learning models [extreme gradient boosting (XGBoost), multivariate adaptive regression spline (MARS) and random forest (RF)] for fiber-reinforced polymer (FRP) composite strain prediction.

^{[3]}MFGWML utilizes three excellent machine learning (ML) algorithms, namely extreme gradient boosting (XGBoost), multivariate adaptive regression splines (MARS), and Bayesian ridge regression (BRR), as base learners to capture the nonlinear relationships.

^{[4]}The development of extreme gradient boosting (XGBoost) and multivariate adaptive regression splines (MARS) models as a robust AI methodology are tested for Vs prediction.

^{[5]}, Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia.

^{[6]}In this study, extreme gradient boosting (XGBoost) approach “as a selective input parameter” was coupled with support vector regression, random forest (RF), and multivariate adaptive regression spline (MARS) models for simulating the RH process.

^{[7]}

## regression splines model

The intention of this research was to try and address the research question 'Is machine learning algorithm, Multivariate Adaptive Regression Splines model, a versatile forecasting model for solar radiation?' The objective of this chapter is to develop a machine learning (ML) algorithm to validate and assess errors for the method used to forecast solar radiation based on historical data.^{[1]}In this study, the multivariate adaptive regression splines model provided a valid reference for the application of and future improvements in SCSGPs.

^{[2]}The aim of this study, therefore, is to investigate the applicability of several machine learning (ML) models (conditional random forest regression, multivariate adaptive regression splines, bagged multivariate adaptive regression splines, model tree M5, K-nearest neighbor, and weighted K-nearest neighbor) in modeling the monthly pan evaporation estimation.

^{[3]}The multivariate adaptive regression splines model was employed to establish the relationship function of the long-term trends for the dependent (TWS) and independent (explanatory) variables consisting of normalized difference vegetation index (NDVI), hydro-climate, and human water withdrawal.

^{[4]}A multivariate adaptive regression splines model is employed that considers foreign exchange (USD-TRY), credit default swap spread, global uncertainty, and global volatility as control variables.

^{[5]}Cats with serum ionized calcium, biochemistry profile and T4 available were screened over 6 years and included in the training set (569 cats) to create a multivariate adaptive regression splines model to calculate piCa.

^{[6]}

## multiple linear regression

The results showed superior capabilities of long short-term memory (LSTM) network in improving 180-min rainfall forecasts at the stations based on a comparison of five different data-driven models, including multiple linear regression (MLR), multivariate adaptive regression splines (MARS), multi-layer perceptron (MLP), basic recurrent neural network (RNN), and LSTM.^{[1]}Different model functional forms were then tested, including multiple linear regressions (MR), multivariate adaptive regression splines (MARS), and support vector regressions (SVR).

^{[2]}The highly correlated covariables were eliminated and the selection of covariables by the level of importance was carried out by the RFE and FFS methods for the Multivariate Adaptive Regression (EARTH), Multiple Linear Regression (MLR), and Random Forest (RF) models.

^{[3]}In this study, different soft computing models (including multiple linear regression (MLR), group method of data handling (GMDH), multivariate adaptive regression splines (MARS), M5P tree model, and random forest (RF)) were employed for the first time in estimation of the Cd value, and their respective prediction performances were analyzed statistically.

^{[4]}The water balance equation was solved with Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) statistical models, fed with readily available data over Comisión Nacional de Actividades Espaciales (CONAE) core site located in Cordoba province, Argentina.

^{[5]}

## boosted regression tree

In this study, seven state-of-the-art machine learning models including boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), multivariate adaptive regression splines (MARS), mixture discriminant analysis (MDA), random forest (RF), and support vector machine (SVM) were applied to model habitat suitability for Ferula gummosa medicinal plant in the Firozkuh County of Tehran.^{[1]}This study modeled land subsidence and created a susceptibility map using multivariate adaptive regression spline (MARS), mixture discriminant analysis (MDA), and boosted regression tree (BRT) machine-learning methods.

^{[2]}This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM).

^{[3]}This study proposed novel integrative flood susceptibility prediction models based on multi-time resampling approaches, random subsampling (RS) and bootstrapping (BT) algorithms, integrated with machine learning models: generalized additive model (GAM), boosted regression tree (BTR) and multivariate adaptive regression splines (MARS).

^{[4]}Then, Boosted Regression Tree (BRT) and Multivariate Adaptive Regression Spline (MARS) algorithms were implemented to model gully erosion susceptibility.

^{[5]}

## support vector regression

In this study, we aimed to implement five artificial intelligence and machine learning regression models such as multivariate adaptive regression splines (MARS), M5' regression tree (M5'), Least square support vector regression (LS-SVR), fuzzy regression based on c-means clustering (FCMR) and regressive convolution neural network with support vector regression (RCNN-SVR) for predicting pipe burst rate and evaluating the performance of these models.^{[1]}This study; i) investigates the suitability of two frequently employed machine learning algorithms in remote sensing, namely, random forests (RFs) and support vector regression (SVR) for fractional snow cover (FSC) estimation from MODIS Terra data, and ii) compares them with the previously proposed artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) methods over an heterogeneous and complex alpine terrain.

^{[2]}, Support Vector Regression SVR, Long Short-term Memory LSTM, Multivariate Adaptive Regression Spline MARS, Persistence) as well as the other alternative hybrid methods (i.

^{[3]}The compared methods are Least Square Support Vector Regression (LSSVR) Multivariate Adaptive Regression Splines (MARS) and M5 Model Tree (M5-Tree).

^{[4]}In this research, three advanced