Using Automated Machine Learning for Spatial Prediction—The Heshan Soil Subgroups Case Study

Peng Liang, Cheng-Zhi Qin () and A-Xing Zhu
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Peng Liang: Key Laboratory of Earthquake Prediction, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
Cheng-Zhi Qin: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
A-Xing Zhu: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Land, 2024, vol. 13, issue 4, 1-12

Abstract: 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.

Keywords: automated method selection; digital soil mapping; soil subgroups; classification methods (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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