The prediction of a spatial variable is of particular importance when analyzing spatial data. The main objective of this study is to evaluate and compare the performance of several prediction-based methods in spatial prediction through a simulation study. The studied methods include ordinary Kriging (OK), along with several neural network methods including Multi-Layer Perceptron network (MLP), Ensemble Neural Networks (ENN), and Radial Basis Function (RBF) network. We simulated several spatial datasets with three different scenarios due to changes in data stationarity and isotropy. The performance of methods was evaluated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Concordance Correlation Coefficient (CCC) indexes. Although the results of the simulation study revealed that the performance of the neural network in spatial prediction is weaker than the Kriging method, but it can still be a good competitor for Kriging.

在分析空间数据时，空间变量的预测尤为重要。本研究的主要目的是通过模拟研究评估和比较几种基于预测的方法在空间预测中的性能。研究的方法包括普通克里金法 (OK)，以及多层感知器网络 (MLP)、集成神经网络 (ENN) 和径向基函数 (RBF) 网络等几种神经网络方法。由于数据平稳性和各向同性的变化，我们模拟了具有三种不同场景的多个空间数据集。使用均方根误差 (RMSE)、平均绝对误差 (MAE) 和一致性相关系数 (CCC) 指标评估方法的性能。