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This is a Quantitative Study study.

October 27, 2025Journal of GeomaticsOpen Access

Evaluating Multivariate Adaptive Regression Splines (MARS) for Modelling Observed Heights Above Mean Sea Level: A Case Study of the Tarkwa Local Geodetic Reference Network in Ghana

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MARS demonstrates improved accuracy in height prediction over traditional methods.
Statistical assessment shows near-perfect correlation with error margins of AMAE: 1.7963E-06 m.
Analysis using field-measured data gathered from a Total Station DTM 122A compares MARS with Polynomial Regression and Kriging models.
Supports advancements in geodetic measurement methods for environmental application.

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Implication

Observational analysis found MARS to improve height measurement accuracy in geodetic networks, indicating better solutions for environmental management.

Cite This Study

Peprah et al. (2025) studied this question.

synapsesocial.com/papers/68ff87e2c8c50a61f2bdced5 https://doi.org/10.58825/jog.2025.19.2.246

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Implication

Observational analysis found MARS to improve height measurement accuracy in geodetic networks, indicating better solutions for environmental management.

Evaluating Multivariate Adaptive Regression Splines (MARS) for Modelling Observed Heights Above Mean Sea Level: A Case Study of the Tarkwa Local Geodetic Reference Network in Ghana

Key Points

Discussion

Implication

Cite This Study

Evaluating Multivariate Adaptive Regression Splines (MARS) for Modelling Observed Heights Above Mean Sea Level: A Case Study of the Tarkwa Local Geodetic Reference Network in Ghana

Key Points

Discussion

Implication

Cite This Study

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