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

文献类型：期刊论文

作者Liang, Peng6; Qin, Cheng-Zhi2,3,4,5; Zhu, A-Xing1,2,4,5
刊名LAND
出版日期2024-04-01
卷号13期号:4页码:12
关键词automated method selection digital soil mapping soil subgroups classification methods
DOI10.3390/land13040551
英文摘要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.
WOS关键词LANDSLIDE SUSCEPTIBILITY ; MODEL SELECTION ; REGRESSION ; CARBON ; GIS
资助项目National Key Research and Development Program of China
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001210890400001
出版者MDPI
资助机构National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/204921]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Qin, Cheng-Zhi
作者单位1.Univ Wisconsin Madison, Dept Geog, Madison, WI 53706 USA
2.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Sch Geog, Nanjing 210097, Peoples R China
3.Shaanxi Normal Univ, Sch Geog & Tourism, Xian 710119, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
6.China Earthquake Adm, Inst Earthquake Forecasting, Key Lab Earthquake Predict, Beijing 100036, Peoples R China
推荐引用方式
GB/T 7714		 Liang, Peng,Qin, Cheng-Zhi,Zhu, A-Xing. Using Automated Machine Learning for Spatial Prediction-The Heshan Soil Subgroups Case Study[J]. LAND,2024,13(4):12.
APA Liang, Peng,Qin, Cheng-Zhi,&Zhu, A-Xing.(2024).Using Automated Machine Learning for Spatial Prediction-The Heshan Soil Subgroups Case Study.LAND,13(4),12.
MLA Liang, Peng,et al."Using Automated Machine Learning for Spatial Prediction-The Heshan Soil Subgroups Case Study".LAND 13.4(2024):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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