The prediction of an accurate geodetic point velocity has great importance in geosciences. The purpose of this work is to explore the predictive capacity of three artificial neural network (ANN) models in predicting geodetic point velocities. First, the multi-layer perceptron neural network (MLPNN) model was developed with two hidden layers. The generalized regression neural network (GRNN) model was then applied for the first time. Afterwards, the radial basis function neural network (RBFNN) model was trained and tested with the same data. Latitude (\(\varphi\)) and longitude (λ) were utilized as inputs and the geodetic point velocities (\({V}_{X}\),\({V}_{Y}\),\({V}_{Z}\)) as outputs to the MLPNN, GRNN, and RBFNN models. The performances of all ANN models were evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (\({\text{R}}^{2}\)). The first investigation demonstrated that it was possible to predict the geodetic point velocities by using all the components as output parameters simultaneously. The other result is that all ANN models were able to predict the geodetic point velocity with satisfactory accuracy; however, the GRNN model provided better accuracy than the MLPNN and RBFNN models. For example, the RMSE and MAE values were 1.77–1.88 mm and 1.44–1.51 mm, respectively, for the GRNN model.

准确的大地测点速度的预测在地球科学中非常重要。这项工作的目的是探索三种人工神经网络（ANN）模型在预测大地测点速度方面的预测能力。首先，开发了具有两个隐藏层的多层感知器神经网络（MLPNN）模型。然后，首次应用了广义回归神经网络（GRNN）模型。之后，使用相同的数据对径向基函数神经网络（RBFNN）模型进行了训练和测试。纬度（\（\ varphi \））和经度（λ）被用作输入，大地测点速度（\（{V} _ {X} \），\（{V} _ {Y} \），\（ {V} _ {Z} \））作为MLPNN，GRNN和RBFNN模型的输出。使用均方根误差（RMSE），平均绝对误差（MAE）和确定系数（\（{\ text {R}} ^ {2} \））评估所有ANN模型的性能。首次调查表明，可以通过同时使用所有分量作为输出参数来预测大地点速度。另一个结果是，所有的ANN模型都能够以令人满意的精度预测大地点速度。但是，GRNN模型提供了比MLPNN和RBFNN模型更好的准确性。例如，对于GRNN模型，RMSE和MAE值分别为1.77-1.88 mm和1.44-1.51 mm。