This study investigates to use GPS/Levelling measurements of Kuwait and four heuristic regression methods including Least Square Support Vector Regression (LSSVR), Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Multivariate Adaptive Regression Splines (MARS) for modelling local geoid undulation. The accuracy of the models was compared by geoid undulation of gravitational observations and Global Geopotential Models (GGMs). The results show that the KRR model is suitable for Kuwait geoid model, its error of percentage is 0.018 and 0.124% relative to gravity and GPS/Levelling geoid undulation models, respectively. Furthermore, the comparison of KRR model with GGMs models signifies its accuracy.

本研究调查使用GPS /科威特水准测量和四种启发式回归方法，包括最小二乘支持向量回归（LSSVR），高斯过程回归（GPR），核岭回归（KRR）和多元自适应回归样条（MARS）进行建模当地大地水准面波动。通过重力观测的大地水准面波动和全球大地势模型（GGM）比较了模型的准确性。结果表明，KRR模型适用于科威特大地水准面模型，相对于重力和GPS /水准大地水准面波动模型，其百分比误差分别为0.018和0.124％。此外，KRR模型与GGMs模型的比较表明了其准确性。