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

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.

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.

A global model of meteoric metals in the atmosphere of Mars

<p>Here we report a global model of meteoric metals including Mg, Fe and Na in the Laboratoire de Météorologie Dynamique (LMD) Mars global circulation model (termed as LMD-Mars-Metals), following on similar work as we have done for the Earth’s atmosphere. The model has been developed by combining three components: the state-of-the-art LMD-Mars model covering the whole atmosphere from the surface to the upper thermosphere (up to ~ 2 x10<sup>-8</sup> Pa or 240 km), a description of the neutral and ion-molecule chemistry of Mg, Fe and Na in the Martian atmosphere (where the high CO<sub>2</sub> abundance produces a rather different chemistry from the terrestrial atmosphere), and a treatment of injection of the metals into the atmosphere as a result of the ablation of cosmic dust particles. The LMD-Mars model contains a detailed treatment of atmospheric physics, dynamics and chemistry from the lower atmosphere to the ionosphere. The model also includes molecular diffusion and considers the chemistry of the C, O, H and N families and major photochemical ion species in the upper atmosphere, as well as improved treatments of the day-to-day variability of the UV solar flux and 15 mm CO<sub>2</sub> cooling under non-local thermodynamic equilibrium conditions. So far, we have incorporated the chemistries of Mg, Fe and Na into LMD-Mars because these metals have different chemistries which control the characteristic features of their ionized and neutral layers in the Martian atmosphere. The Mg chemistry has 4 neutral and 6 ionized Mg-containing species, connected by 25 neutral and ion-molecule reactions. The corresponding Fe chemistry has 39 reactions with 14 Fe-containing species. Na chemistry has 7 neutral and only 2 ionized Na-containing species, with 32 reactions. The injection rate of these metals as a function of height is pre-calculated from the Leeds Chemical Ablation Model (CABMOD) combined with an astronomical model which predicts the dust from Jupiter Family and Long Period comets, as well as the asteroid belt, in the inner solar system. The LMD-Mars-Metals model has been run for several full Martian years under different surface dust scenarios to investigate the impact of high atmospheric dust loadings on the modelled metal layers. The model has been evaluated against Mg<sup>+</sup> observations from IUVS (Imaging UV Spectrometer) and NGIMS (Neutral Gas Ion Mass Spectrometer) instruments on NASA’s Mars Atmosphere and Volatile Evolution Mission (MAVEN) spacecraft. We have also carried out other sensitivity experiments with different seasonality/altitude/latitudinal varying of Meteoric Input Function (MIF) of these metals in the model. These sensitivity results will be discussed.  </p>

A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Accurate and reliable prediction of relative humidity is of great importance in all fields concerning global climate change. The current study has employed Multivariate Adaptive Regression Spline (MARS) and M5 Tree (M5T) models to predict the relative humidity in the Hunza River basin, Pakistan. Both the models provided the best prediction for the input scenario S6 (RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3). The statistical analysis displayed that the MARS model provided a better prediction of relative humidity as compared to M5T at all meteorological stations, especially, at Ziarat followed by Khunjerab and Naltar. The values of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were (5.98%, 5.43%, and 0.808) for Khunjerab; (6.58%, 5.08%, and 0.806) for Naltar; and (5.86%, 4.97%, 0.815) for Ziarat during the testing of MARS model whereas, the values were (6.14%, 5.56%, and 0.772) for Khunjerab; (6.19%, 5.58% and 0.762) for Naltar and (6.08%, 5.46%, 0.783) for Ziarat during the testing of M5T model. Both the models performed slightly better in training as compared to the testing stage. The current study encourages future research to be conducted at high altitude basins for the prediction of other meteorological variables using machine learning tools.

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

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.

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

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.

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

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).