Prediction of compaction parameters of coarse grained soil using multivariate adaptive regression splines (MARS)

Khuntia, Sunil; Mujtaba, Hassan; Patra, Chittaranjan; Farooq, Khalid; Sivakugan, Nagaratnam; Das, Braja M.

doi:10.1179/1939787914y.0000000061

Cited by 45 publications

(17 citation statements)

References 13 publications

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“…In case of SVM, Han and Jiang [142] performed a thorough analysis with different kernels, and they found that Gaussian kernels can encounter overfitting issues due to C parameter, which controls the misclassification (i.e., if the C parameter is large, a smaller margin hyperplane tends to minimize misclassifications); thus, the forced accuracy raises the chance of overfitting, too. MARS was studied by Khuntia et al [143], and they found that due to its two-step prediction process, and especially the second step of backward variable selection (pruning) with removing the variables having the least contribution helps to handle the overfit. To sum it up, besides the potential risk of overfitting that increases when we apply many input variables, all the above cited studies agreed on the relevance of RKCV (or simple KCV) which is an important element for fine tuning ML algorithms in order to find the best hyperparameters, and also for minimizing the overfitting.…”

Section: Discussionmentioning

confidence: 99%

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

et al. 2020

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Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.

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Section: Discussionmentioning

confidence: 99%

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

et al. 2020

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“…Number of samples per group was calculated by using "clinical c" software. Sample size for each group was calculated by using the mean standard deviation values obtained from the study by ((Khuntia et al 2015). The values of threshold limit, g power, and confidence interval were 0.05, 80% and 95% respectively and the sample size required was estimated to be 17.…”

Section: Methodsmentioning

confidence: 99%

Study on the Compaction Characteristics of Soil and Marble Dust Composite as a Potential Green Building Material

Sabari¹,

Ramalakshmi²

2021

revistageintec

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The aim of the study is to evaluate the effects of adding the waste marble dust to soil on the compaction characteristics of the soil. Standard Proctor test is conducted for two groups of samples (Group 1 without marble dust group N = 17; group 2: Soil with marble dust N = 17). The test results show that the optimum moisture content (OMC) of soil decreases and the maximum dry density (MDD) increases due to the addition of marble dust with the soil. There is a significance difference between the two groups of soil as the value of p<0.05. Soil with marble dust gets compacted more than the soil without marble dust. Thus the marble dust can be used as an additive material so as to improve the compaction characteristics of the soil.

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“…This strategy made the MARS method more advantageous and flexible than the other statistical methods in multivariate modeling studies [53]. More details about the MARS and its implementation can be found in [54][55][56].…”

Section: Multivariate Adaptive Regression Splines (Mars) Methodsmentioning

confidence: 99%

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

Nacar

Bayram

BAKİ

et al. 2020

Water

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The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

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Prediction of compaction parameters of coarse grained soil using multivariate adaptive regression splines (MARS)

Cited by 45 publications

References 13 publications

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

Study on the Compaction Characteristics of Soil and Marble Dust Composite as a Potential Green Building Material

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

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