Predicting CBR value of stabilized pond ash with lime and lime sludge using multivariate adaptive regression splines

Dry Density ◽

Regression Splines ◽

Maximum Dry Density ◽

Pond Ash ◽

Mars Model ◽

Accuracy Comparison ◽

Adaptive Regression ◽

Lime Sludge ◽

Abstract In this study, multivariate adaptive regression splines (MARS) model with order two and three were developed for predicting the California bearing capacity (CBR) value of pond ash stabilized with lime and lime sludge. To this aim, the model had five variables named maximum dry density, optimum moisture content, lime percentage, lime sludge percentage, and curing period as inputs, and CBR as output variable. MARS-O3 has the best results, which its R2 stood at 0.9565 and 0.9312, and PI 0.0709 and 0.1061 for the training and testing phases, respectively. In both developed models, the estimated CBR values in training and testing stages specify acceptable agreement with experimental results, representing the workability of proposed equations for predicting the CBR values with high accuracy. Comparison of two developed equations supplied that MARS-O3 has a better result than MARS-O2. Based on error curves, the MARS-O3 model results in the lowest error percentage in the CBR predicting process, providing roughly accurate prediction than those of the rest developed methods specified. Therefore, MARS-O3 could be recognized as the proposed model.

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Energies ◽

10.3390/en12081416 ◽

2019 ◽

Vol 12 (8) ◽

pp. 1416 ◽

Cited By ~ 13

Author(s):

Mohammad Rezaie-Balf ◽

Niloofar Maleki ◽

Sungwon Kim ◽

Ali Ashrafian ◽

Fatemeh Babaie-Miri ◽

...

Keyword(s):

Solar Radiation ◽

Search Algorithm ◽

Ensemble Empirical Mode Decomposition ◽

Regression Splines ◽

Mode Decomposition ◽

Mars Model ◽

Adaptive Regression ◽

Adaptive Noise ◽

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka

Jurnal Matematika MANTIK ◽

10.15642/mantik.2021.7.1.51-58 ◽

2021 ◽

Vol 7 (1) ◽

pp. 51-58

Author(s):

Siti Hadijah Hasanah

Keyword(s):

Confidence Interval ◽

Marital Status ◽

Explanatory Variable ◽

P Value ◽

Regression Splines ◽

Response Variable ◽

Mars Model ◽

Adaptive Regression ◽

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. This study consists of 9 variables, namely gender, age, education, marital status, job, initial registration year, number of registrations, credits, and GPA, but after modeling using the MARS method, the explanatory variable can affect the response variable is the initial registration year. Several registrations, GPA, and credits. Based on the results of the R output and using a 95% confidence interval, each base 1 to 10 function is partially significant with the p-value of the base 1-10 function being smaller than 0.05 and simultaneously with a smaller p-value. of 0.05, so that the above model has a significant effect partially or simultaneously on the response variable. From these results, it is concluded that the MARS model is suitable for determining the factors that affect the active status of students.

The Learning of Multivariate Adaptive Regression Splines (MARS) Model in Rainfall-Runoff Processes at Pahang River Catchment

Annals of Valahia University of Targoviste Geographical Series ◽

10.2478/avutgs-2018-0018 ◽

2018 ◽

Vol 18 (2) ◽

pp. 161-167

Author(s):

D.A. Halid ◽

I. Atan ◽

J. Jaafar ◽

Y. Ashaari ◽

S.N. Mohamed ◽

...

Keyword(s):

Regression Splines ◽

Rainfall Runoff ◽

Performance Modelling ◽

Data Mining Technique ◽

Data Set ◽

River Catchment ◽

Mars Model ◽

Adaptive Regression ◽

Abstract Recently, a novel data mining technique, Multivariate Adaptive Regression Splines (MARS) has begun attracted attention from several hydrological researchers because their application is relatively new in modelling hydrological processes. The power of this approach has been proven in variety learning problems such as financial analysis, species distributions modelling, and doweled pavement performance modelling. Therefore, the objective of this paper is to investigate the performance of MARS model in capture the rainfall-runoff processes at river catchment of Malaysia. Pahang River has been selected as area of study. 30-years data set of daily rainfall and runoff at upstream tributaries of Pahang River were used to developed and validate the capability of MARS model in flood prediction. The effect of different length of record data to performance of MARS model was also examined by arranged the data into 5-years data set, 10 years data set, 20 years data set, and 30 years data set. All these data sets used 1-year data of 2003 for validation process while the others were applied for calibration. Simulation results showed that MARS model was able to learn the rainfall-runoff processes in Pahang River catchment and the model performance improved due to the longer period of data.

Poverty in Central Java using Multivariate Adaptive Regression Splines and Bootstrap Aggregating Multivariate Adaptive Regression Splines

CAUCHY ◽

10.18860/ca.v6i4.10871 ◽

2021 ◽

Vol 6 (4) ◽

pp. 238-245

Author(s):

Ria Dhea Layla Nur Karisma ◽

Juhari Juhari ◽

Ramadani A Rosa

Keyword(s):

Regression Splines ◽

Central Java ◽

Mars Model ◽

Influence Variable ◽

Adaptive Regression ◽

Variable Population ◽

Influential Variable ◽

Adaptive Regression Splines ◽

Central Java Province

Population poverty is one of the serious problems in Indonesia. The percentage of population poverty used as a means for a statistical instrument to be guidelines to create standard policies and evaluations to reduce poverty. The aims of the research are to determine model population poverty using MARS and Bagging MARS then to understand the most influence variable population poverty of Central Java Province in 2018. The result of this research is the Bagging MARS model showed better accuracy than the MARS model. Since, GCV value in the Bagging MARS model is 0,009798721 and GCV value in the MARS model is 6,985571. The most influential variable poverty population of Central Java Province in 2018 in the MARS model is the percentage of the old school expectation rate (X9). Then, the most influential variable in the Bagging MARS model is the number of diarrhea (X1).

Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Open Geosciences ◽

10.1515/geo-2020-0198 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1263-1273

Author(s):

Zhihao Liao ◽

Zhiwei Liao

Keyword(s):

Slope Stability ◽

Construction Projects ◽

Model Development ◽

Regression Splines ◽

Stability Assessment ◽

Data Set ◽

Mars Model ◽

Adaptive Regression ◽

AbstractSlope stability assessment is a critical concern in construction projects. This study explores the use of multivariate adaptive regression splines (MARS) to capture the intrinsic nonlinear and multidimensional relationship among the parameters that are associated with the evaluation of slope stability. A comparative study of machine learning solutions for slope stability assessment that relied on backpropagation neural network (BPNN) and MARS was conducted. One data set with actual slope collapse events was utilized for model development and to compare the performance of BPNN and MARS. Research results suggest that BPNN and MARS models can model the relationship between the safety factor and the slope parameters. Also, the MARS model has the advantages of computational efficiency and easy interpretation.