Recently, numerous spatial prediction methods with diverse characteristics have been developed. Selecting an appropriate spatial prediction method, along with its data preprocessing and parameter settings, presents a challenging task for many users, especially for non-experts. This paper addresses this challenge by exploring the potential of automated machine learning method proposed in artificial intelligent domain to automatically determine the most suitable method among various machine learning methods. As a case study, the automated machine learning method was applied to predict the spatial distribution of soil subgroups in Heshan farm. A total of 110 soil samples and 10 terrain variables were utilized in the designed experiments. To evaluate the performance, the proposed method was compared to each machine learning method with default parameters values or parameters determined by expert knowledge. The results showed that the proposed method typically achieved higher accuracy scores than the two alternative methods. This suggests that automated machine learning performs effectively in scenarios where numerous machine learning methods are available and offers practical utility in reducing the dependence on users’ expertise in spatial prediction. However, a more robust automated framework should be developed to encompass a broader range of spatial prediction methods, such as spatial statistic methods, rather than only focusing on machine learning methods.

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使用自动机器学习进行空间预测——鹤山土壤亚群案例研究

最近，已经开发了许多具有不同特征的空间预测方法。选择合适的空间预测方法及其数据预处理和参数设置，对许多用户（尤其是非专家）来说是一项具有挑战性的任务。本文通过探索人工智能领域提出的自动化机器学习方法的潜力来解决这一挑战，以自动确定各种机器学习方法中最合适的方法。作为案例研究，应用自动化机器学习方法来预测鹤山农场土壤亚类的空间分布。设计的实验中总共使用了 110 个土壤样本和 10 个地形变量。为了评估性能，将所提出的方法与具有默认参数值或由专家知识确定的参数的每种机器学习方法进行比较。结果表明，所提出的方法通常比两种替代方法获得更高的准确度分数。这表明自动化机器学习在有多种机器学习方法可用的场景中表现有效，并在减少对用户空间预测专业知识的依赖方面提供了实用性。然而，应该开发一个更强大的自动化框架来涵盖更广泛的空间预测方法，例如空间统计方法，而不是仅仅关注机器学习方法。