scholarly journals Prediction of aeration efficiency of parshall and modified venturi flumes: application of soft computing versus regression models

2021 ◽  
Author(s):  
Parveen Sihag ◽  
Omer Faruk Dursun ◽  
Saad Shauket Sammen ◽  
Anurag Malik ◽  
Anita Chauhan
Keyword(s):  
Soft Computing ◽  
Multivariate Adaptive Regression Splines ◽  
Oxygen Deficit ◽  
Group Method ◽  
Mars Model ◽  
Soft Computing Techniques ◽  
Aeration Efficiency ◽  
Adaptive Regression ◽  
Input Variables ◽  
Adaptive Regression Splines

Abstract In this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE20) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE20 at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 at Parshall and Modified Venturi flumes.

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

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1416 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Niloofar Maleki ◽  
Sungwon Kim ◽  
Ali Ashrafian ◽  
Fatemeh Babaie-Miri ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Search Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Mode Decomposition ◽  
Mars Model ◽  
Adaptive Regression ◽  
Adaptive Noise ◽  
Adaptive Regression Splines

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.

scholarly journals Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

2020 ◽  
pp. 1-14
Author(s):  
Mohammed Okoe Alhassan ◽  
Michael Boakye Osei
Keyword(s):  
Random Forest ◽  
Polymethyl Methacrylate ◽  
Polymeric Materials ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Regression Splines ◽  
Soft Computing Techniques ◽  
Flammability Characteristics ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Soft-computing techniques for fire safety parameter predictions in flammability studies are essential for describing a material fire behaviour. This study proposed, two novel Artificial Intelligence developed models, Multivariate Adaptive Regression Splines (MARS) and Random Forest (RF) methods, to model and predict peak heat release rate (pHRR) of Polymethyl methacrylate (PMMA) from Microscale Combustion Calorimetry (MCC) experiment. From the statistical analysis, MARS presented the highest coefficient of determination (R2) values of (0.9998) and (0.9996) for training and testing respectively, with low MAD, MAPE and RMSE values. Comparatively, MARS outperformed RF in the predictions of pHRR, through its model algorithms that generated optimized equations for pHRR predictions, covering all non-linearity points of the experimental data. Amongst the input variables (sample mass, THR, HRC, pTemp and pTime), heating rate (β), highly influenced pHRR outcome predictions from MARS and RF models. However, to validate the performance and applicability of the proposed models. Results of MARS and RF were benchmarked with that from Artificial Neural Network (ANN) methods. The MARS and RF models observed the least error deviation when compared with pHRR results for PMMA from the ANN models. This study therefore, recommends the adoption of MARS and RF in the predictions of flammability characteristics of polymeric materials.

scholarly journals IRI Sensitivity to the Influence of Surface Distress on Flexible Pavements

Coatings ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 271 ◽  
Author(s):  
Ufuk Kırbaş
Keyword(s):  
Multivariate Adaptive Regression Splines ◽  
Asphalt Pavements ◽  
International Roughness Index ◽  
Pavement Distress ◽  
Road Users ◽  
Adaptive Regression ◽  
Surface Distress ◽  
Input Variables ◽  
Adaptive Regression Splines ◽  
High Degree

Today, authorities responsible for the operation of highways aim to provide comfort to road users as well as safety while driving. While driving, the most important component that determines comfort for road users is the pavement. The relative effects of various surface distress types in bituminous, hot mixed asphalt pavements on the International Roughness Index (IRI) component—used to evaluate the present performance, and hence the comfort, of pavements—are determined in this study. The presence of only one type of surface distress is very difficult to achieve in practice, especially in regards to pavements where a high degree of deterioration is observed. The presence of different types of surface distress in road pavements, due to similar problems in very close positions and even in nested forms, makes it difficult to assess this issue. The relationships between surface distress and IRI have been modelled to overcome this challenge. To this end, the Multivariate Adaptive Regression Splines (MARS) modelling approach, which is very successful in investigating the relationships between a large number of independent variables and dependent variables, has been used. The sensitivities of the surface distress inputs are evaluated singularly by means of a model with 29 input variables calibrated using the pavement distress data collected in 3295 highway pavement sections. As a result of this analysis, the sensitivity of surface distress inputs collected, as an area, has been determined to have an effect on the increase in IRI. The results are interpreted with the help of figures and tables.

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

2021 ◽  
Vol 7 (1) ◽  
pp. 51-58
Author(s):  
Siti Hadijah Hasanah
Keyword(s):  
Confidence Interval ◽  
Marital Status ◽  
Explanatory Variable ◽  
Multivariate Adaptive Regression Splines ◽  
P Value ◽  
Regression Splines ◽  
Response Variable ◽  
Mars Model ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

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

2018 ◽  
Vol 18 (2) ◽  
pp. 161-167
Author(s):  
D.A. Halid ◽  
I. Atan ◽  
J. Jaafar ◽  
Y. Ashaari ◽  
S.N. Mohamed ◽  
...  
Keyword(s):  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Rainfall Runoff ◽  
Performance Modelling ◽  
Data Mining Technique ◽  
Data Set ◽  
River Catchment ◽  
Mars Model ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

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.

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

2021 ◽  
Author(s):  
Shen Xing-xing ◽  
Cao Wei-wei ◽  
Li Kai
Keyword(s):  
Multivariate Adaptive Regression Splines ◽  
Dry Density ◽  
Regression Splines ◽  
Maximum Dry Density ◽  
Pond Ash ◽  
Mars Model ◽  
Accuracy Comparison ◽  
Adaptive Regression ◽  
Lime Sludge ◽  
Adaptive Regression Splines

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.

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

CAUCHY ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 238-245
Author(s):  
Ria Dhea Layla Nur Karisma ◽  
Juhari Juhari ◽  
Ramadani A Rosa
Keyword(s):  
Multivariate Adaptive Regression Splines ◽  
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).

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

2020 ◽  
Vol 12 (1) ◽  
pp. 1263-1273
Author(s):  
Zhihao Liao ◽  
Zhiwei Liao
Keyword(s):  
Slope Stability ◽  
Construction Projects ◽  
Model Development ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Stability Assessment ◽  
Data Set ◽  
Mars Model ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

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.

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