Gully Erosion Susceptibility in Black Soil Region of Northeast China Based on Four Machine Learning Models

Li Xinyu; Zheng Fenli; Wu Binglong; Li Rong

doi:10.13870/j.cnki.stbcxb.2026.04.013

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Gully Erosion Susceptibility in Black Soil Region of Northeast China Based on Four Machine Learning Models

Technical Method | 更新时间：2026-09-10

- Gully Erosion Susceptibility in Black Soil Region of Northeast China Based on Four Machine Learning Models
- Journal of Soil and Water Conservation Vol. 40, Issue 4, Pages: 420-429(2026)
- 作者机构：
  
  1.西北农林科技大学水土保持科学与工程学院（水土保持研究所），水土保持与荒漠化整治全国重点实验室，陕西杨陵712100
  2.中国科学院水利部水土保持研究所，陕西杨陵712100
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2026.04.013
  CLC： S157.1
- CSTR：32310.14.stbcxb.2026.04.013
- Received：03 January 2026，
  
  Revised：2026-02-18，
  
  Accepted：15 March 2026，
  
  Online First：25 March 2026，
  
  Published：28 August 2026
- 稿件说明：
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李欣雨，郑粉莉，毋冰龙，等.基于4种机器学习模型的东北黑土区沟蚀敏感性［J］.水土保持学报，2026，40（4）：420-429.

Li Xinyu， Zheng Fenli， Wu Binglong， et al. Gully erosion susceptibility in black soil region of northeast China based on four machine learning models［J］. Journal of Soil and Water Conservation， 2026， 40（4）：420-429.
李欣雨，郑粉莉，毋冰龙，等.基于4种机器学习模型的东北黑土区沟蚀敏感性［J］.水土保持学报，2026，40（4）：420-429. DOI： 10.13870/j.cnki.stbcxb.2026.04.013. CSTR： 32310.14.stbcxb.2026.04.013.

Li Xinyu， Zheng Fenli， Wu Binglong， et al. Gully erosion susceptibility in black soil region of northeast China based on four machine learning models［J］. Journal of Soil and Water Conservation， 2026， 40（4）：420-429. DOI： 10.13870/j.cnki.stbcxb.2026.04.013. CSTR： 32310.14.stbcxb.2026.04.013.

摘要

目的

构建沟蚀敏感评价模型，为东北黑土区沟蚀发生预警及其精准治理提供重要科学依据。

方法

以典型厚层黑土区的黑龙江省克东县为研究区，基于0.4 m高分辨率遥感影像提取侵蚀沟，通过方差膨胀因子剔除冗余变量，选取涵盖线性、非线性及集成学习架构的4种机器学习模型（逻辑回归-LR、支持向量机-SVM、随机森林-RF、极端梯度提升-XGBoost）进行对比，并引入SHAP（Shapley Additive exPlanations）可解释性模型量化各影响因子对沟蚀敏感性的贡献作用。

结果

1） XGBoost对沟蚀敏感性的预测能力最佳（训练集AUC=0.81，测试集AUC=0.75），其次是SVM与RF（测试集AUC=0.74），而LR对沟蚀敏感性的预测能力最弱（AUC=0.67）。2） 4个模型预测的沟蚀敏感性在县域尺度均呈周边低、中部高的空间分布特征，但沟蚀敏感性等级则存在明显差异。RF模型预测的高沟蚀敏感性占比最大，其值为45.0%；SVM和XGBoost模型预测的研究区沟蚀敏感性均以中、极低为主，二者占比分别为56.2%和59.2%；而LR预测结果则研究区以低沟蚀敏感性为主，其值为39.4%。3） SHAP分析结果表明，DEM（数字高程模型）是影响沟蚀敏感性的主导因子（SHAP为0.55），且具有显著正向作用；SPI（径流功率指数）对沟蚀敏感性的正向驱动作用仅次于DEM（SHAP为0.30），NDVI则对沟蚀敏感性呈现明显负向作用（SHAP为0.37）。

结论

在厚层黑土区克东县，基于4种机器学习模型评价的沟蚀敏感性等级存在明显差异，XGBoost模型与SHAP方法融合，增强机器学习模型预测沟蚀敏感性的可解释性；且沟蚀敏感性受“地形主控-植被调控-汇流水动力触发”耦合作用的共同驱动，研究结果加深对沟蚀发生的认知，并为黑土区侵蚀沟治理的措施优化提供理论指导。

Abstract

Objective

This study aims to construct a gully erosion susceptibility assessment model in order to provide an important scientific basis for early warning of gully erosion occurrence and its precise management in the black soil region of Northeast China.

Methods

Kedong County in Heilongjiang Province， a typical thick-layer black soil region， was selected as the study area. Erosion gullies were extracted based on 0.4 m high-resolution remote sensing images. Redundant variables were eliminated using the variance inflation factor （VIF）. Four machine learning models covering linear， nonlinear， and ensemble learning architectures-logistic regression （LR）， support vector machine （SVM）， random forest （RF）， and extreme gradient boosting （XGBoost）-were compared. The SHAP （Shapley Additive exPlanations） interpretability model was introduced to quantify the contribution of each influencing factor to gully erosion susceptibility.

Results

1） XGBoost demonstrated the best predictive capability for gully erosion susceptibility （training set AUC=0.81， test set AUC=0.75）， followed by the SVM and RF （test set AUC=0.74）， while LR showed the weakest predictive capability （AUC=0.67）. 2） The gully erosion susceptibility predicted by the four models all exhibited a spatial distribution pattern of "low in the periphery and high in the center" at the county scale， but there were significant differences in the susceptibility levels. The RF model predicted the largest proportion of high gully erosion susceptibility， accounting for 45.0%. The gully erosion susceptibility predicted by the SVM and XGBoost models in the study area was predominantly moderate and very low levels， accounting for 56.2% and 59.2%， respectively. In contrast， the LR prediction results indicated that low gully erosion susceptibility was dominant， covering 39.4% of the study area. 3） SHAP analysis revealed that the digital elevation model （DEM） was the dominant factor influencing gully erosion susceptibility （SHAP value=0.55）， exhibiting a significant positive effect. The stream power index （SPI） had the second strongest positive driving effect on gully erosion susceptibility （SHAP value=0.30）， while the normalized difference vegetation index （NDVI） showed a clear negative effect on gully erosion susceptibility （SHAP value=0.37）.

Conclusion

In the thick-layer black soil region of Kedong County， significant differences are observed in the gully erosion susceptibility levels evaluated by the four machine learning models. The integration of the XGBoost model with the SHAP method enhances the interpretability of machine learning models in predicting gully erosion susceptibility. Moreover， gully erosion susceptibility is jointly driven by the coupled effects of "terrain-dominated control， vegetation regulation， and confluence hydrodynamics triggering". These findings deepen the understanding of gully erosion occurrence and provide theoretical guidance for optimizing gully erosion control measures in black soil regions.

关键词

Keywords

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