Machine learning ensemble technique for exploring soil type evolution
Abstract Machine learning has shown great potential in predicting soil properties, but individual models are often prone to overfitting, limiting their generalization. Ensemble models address this challenge by combining the strengths of multiple algorithms. This study applies a voting-based ensemble...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10608-8 |
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| author | Xiangyuan Wu Kening Wu Shiheng Hao Er Yu Jinghui Zhao Yan Li |
| author_facet | Xiangyuan Wu Kening Wu Shiheng Hao Er Yu Jinghui Zhao Yan Li |
| author_sort | Xiangyuan Wu |
| collection | DOAJ |
| description | Abstract Machine learning has shown great potential in predicting soil properties, but individual models are often prone to overfitting, limiting their generalization. Ensemble models address this challenge by combining the strengths of multiple algorithms. This study applies a voting-based ensemble model (VEM), integrating Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), to gain a deeper understanding of soil type evolution, which is crucial for land management and soil conservation. The research, conducted in the Tongzhou District of Beijing, uses 5,000 sampling points selected via genetic algorithms for model training, 237 surface samples for consistency testing, and 97 profiles for field validation. The VEM demonstrates high accuracy and robustness, producing a detailed soil type map and identifying key trends in soil type evolution. This study highlights the effectiveness of ensemble models in understanding soil evolution and offers valuable insights into soil system dynamics. |
| format | Article |
| id | doaj-art-e2cdcb01df0949afa2417a9f21dae920 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e2cdcb01df0949afa2417a9f21dae9202025-08-20T03:05:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-10608-8Machine learning ensemble technique for exploring soil type evolutionXiangyuan Wu0Kening Wu1Shiheng Hao2Er Yu3Jinghui Zhao4Yan Li5School of Public Affairs, Institute of Land Science and Property, Zhejiang UniversitySchool of Land Science and Technology, China University of GeosciencesSchool of Land Science and Technology, China University of GeosciencesSchool of Public Affairs, Institute of Land Science and Property, Zhejiang UniversitySchool of Public Affairs, Institute of Land Science and Property, Zhejiang UniversitySchool of Public Affairs, Institute of Land Science and Property, Zhejiang UniversityAbstract Machine learning has shown great potential in predicting soil properties, but individual models are often prone to overfitting, limiting their generalization. Ensemble models address this challenge by combining the strengths of multiple algorithms. This study applies a voting-based ensemble model (VEM), integrating Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), to gain a deeper understanding of soil type evolution, which is crucial for land management and soil conservation. The research, conducted in the Tongzhou District of Beijing, uses 5,000 sampling points selected via genetic algorithms for model training, 237 surface samples for consistency testing, and 97 profiles for field validation. The VEM demonstrates high accuracy and robustness, producing a detailed soil type map and identifying key trends in soil type evolution. This study highlights the effectiveness of ensemble models in understanding soil evolution and offers valuable insights into soil system dynamics.https://doi.org/10.1038/s41598-025-10608-8Soil evolutionVoting-based ensemble modelMachine learning algorithmsSoil type prediction |
| spellingShingle | Xiangyuan Wu Kening Wu Shiheng Hao Er Yu Jinghui Zhao Yan Li Machine learning ensemble technique for exploring soil type evolution Scientific Reports Soil evolution Voting-based ensemble model Machine learning algorithms Soil type prediction |
| title | Machine learning ensemble technique for exploring soil type evolution |
| title_full | Machine learning ensemble technique for exploring soil type evolution |
| title_fullStr | Machine learning ensemble technique for exploring soil type evolution |
| title_full_unstemmed | Machine learning ensemble technique for exploring soil type evolution |
| title_short | Machine learning ensemble technique for exploring soil type evolution |
| title_sort | machine learning ensemble technique for exploring soil type evolution |
| topic | Soil evolution Voting-based ensemble model Machine learning algorithms Soil type prediction |
| url | https://doi.org/10.1038/s41598-025-10608-8 |
| work_keys_str_mv | AT xiangyuanwu machinelearningensembletechniqueforexploringsoiltypeevolution AT keningwu machinelearningensembletechniqueforexploringsoiltypeevolution AT shihenghao machinelearningensembletechniqueforexploringsoiltypeevolution AT eryu machinelearningensembletechniqueforexploringsoiltypeevolution AT jinghuizhao machinelearningensembletechniqueforexploringsoiltypeevolution AT yanli machinelearningensembletechniqueforexploringsoiltypeevolution |