A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

Samui, Pijush; Kothari, D.P.

doi:10.4186/ej.2012.16.5.69

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…Results are achieved by eliminating redundant basis functions from Equation (11). Generalized Cross-Validation (GCV) is used in the MARS model to remove redundant basis functions (Samui & Kothari, 2012). Equation 12GCV expression (Craven & Wahba, 1979):…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Artificial Intelligence Techniques for Predicting Tidal Effects Based on Geographic Locations in Ghana

Issaka

Kumi-Boateng

2020

Geodesy and Cartography

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Tidal forces as a result of attraction of external bodies (Sun, Moon and Stars) through gravity and are a source of noise in many geoscientific field observations. The solid earth tides cause deformation. This deformation results in displacement in geographic positions on the surface of the earth. The displacement due to tidal effects can result in deformation of engineering structures, loss of lives, and economic cost. Tidal forces also help in detecting other environmental and tectonic signals. This study quantifies the effects of solid earth tides on stationary survey controls in five regions of Ghana. The study is in two stages: firstly, the solid earth tides were estimated for each control by a geometric approach (combining Navier’s equation of motion and Love theories). Secondly, estimation using two artificial intelligence methods (Multivariate Adaptive Regression Splines (MARS) and Backpropagation Artificial Neural Network (ANN)). Based on statistical indices of Mean Square Error (MSE) and Correlation Coefficient (R), BPANN, and MARS models can be used as a realistic alternative technique in quantifying solid earth tides for the study area. The MSE and R (MSE; BPANN = 1.3249 × 10–04 and MSE; MARS = 2.2052 × 10–06; R; BPANN = –0.6067 and R; MARS 0.6570) values indicate that MARS outperforms BPANN in quantifying solid earth tides in the study area. BPANN and MARS can be used as an efficient tool for quantifying tidal values based on geographic positions for geodetic deformation studies within the study area.

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Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Artificial Intelligence Techniques for Predicting Tidal Effects Based on Geographic Locations in Ghana

Issaka

Kumi-Boateng

2020

Geodesy and Cartography

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“…There are mainly two approaches for fuzzy inference systems, namely the approaches of Mamdani and Sugeno [23,24]. It has many applications in controlling, predicting and different fields [25][26][27][28]. In order to prevent the manuscript to become voluminous, the classic ANFIS steps are not explained in the present study, and one can find them in the existing studies [6,20,24].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Pirayesh¹,

Farzin²,

Malekani³

et al. 2019

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Traditionally, climate conditions have been one of the influential factors in population growth in worldwide. Hence, predicting these conditions can be an important step to improve life conditions in worldwide. In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature. Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series. First, the most appropriate inputs were selected for the models using sensitivity analysis. After that, long-term air temperatures (1 month, 1-, 2-and 3-year ahead) were modeled. Results showed that: 1) the given algorithms had acceptable results in improving the models' performance in forecasting minimum, mean and maximum air temperatures. Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R 2 ), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals.

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“…Where d is a penalty for each BFs included into the model and H is the number of basis functions in Equation (7) [37,61].…”

Section: Resources and Methods Usedmentioning

confidence: 99%

“…In order to mitigate this problem, the second step that is the backward algorithm prevents overfitting by removing redundant basis functions from Equation (7). The MARS model adopts Generalized Cross-Validation (GCV) to delete the redundant basis functions [61]. The expression of GCV is written as given by Equation (8) [62]:…”

Section: Resources and Methods Usedmentioning

confidence: 99%

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

Peprah¹,

Mensah²,

Akresi³

2017

JGG

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In Ghana's local Geodetic Reference Network, the standard forward transformation equation has played a major role in coordinate transformation between World Geodetic System 1984 (WGS84) and local geodetic datum. Thus, it is an initial step in forward conversion of geodetic coordinates (ϕ , λ, h) to Cartesian coordinates (X, Y, Z) in transformation from global to local datum and vice versa. Several studies in the recent decades have been conducted on converting Cartesian coordinates to geodetic coordinates (reverse procedure) through the utilisation of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on the existing literature covered pertaining to this present study, it was found that the existing knowledge do not fully adhere to the issue of evaluating alternative techniques in the case of the forward conversion. Hence, the aim of this present study was to explore the coordinate conversion performance of the Multivariate Adaptive Regression Splines ( ). The statistical findings revealed that the MARS and MLR offered satisfactory prediction of Cartesian coordinates. However, the MLR compared to MARS showed better stability and more accurate prediction results. From the results of this present study, the main conclusion drawn is that, MLR provides a promising alternative in the forward conversion of geodetic coordinates into Cartesian coordinates. Therefore, the capability of MLR as a powerful tool for solving majority of function approximation problems in mathematical geodesy has been demonstrated in this present study.

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A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

Cited by 16 publications

References 20 publications

Artificial Intelligence Techniques for Predicting Tidal Effects Based on Geographic Locations in Ghana

Artificial Intelligence Techniques for Predicting Tidal Effects Based on Geographic Locations in Ghana

Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

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