Abstract Gully erosion is a severe form of soil erosion that results in a wide range of environmental problems such as, dams’ sedimentation, destruction of transportation and energy transmission lines, decreasing and losing farmland productivity, and land degradation. The main objective of this study is to accurately map the areas prone to gully erosion, by developing two machine learning (ML) models, namely artificial neural network (ANN) and random forest (RF) models within 4-fold cross-validation (CV). Moreover, we used the multi-collinearity analysis to select 11 variables among 15 conditioning factors to train the ML models for gully erosion susceptibility mapping (GESM). Lamerd county, Iran, is chosen for a study area because Lamerd county is one of the most affected areas by gully erosion in this country. From 232 gully samples, 75% was used to train the two ML models and the rest of the samples (25%) were used to validate the generated GEMSs using 4-fold CV. The RF model produced a higher accuracy with an accuracy value of 93%. The GEMS generated by the RF model shows that the areas classified as highly vulnerable and very highly vulnerable are 1,869 ha and 5,148 ha, respectively. Results from the two models indicated that the most vulnerable land use/landcover class is bare land because of the low vegetation cover. The outcome of this study can help managers in Lamerd county to mitigate the soil erosion problem and prevent future gully erosion by taking preventive measures.

中文翻译：

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使用通过多重共线性分析和 K 折交叉验证优化的机器学习方法的沟蚀敏感性映射 (GESM)

摘要 沟蚀是一种严重的土壤侵蚀形式，会导致大坝淤积、交通和能源输送线路破坏、农田生产力下降和丧失、土地退化等一系列环境问题。本研究的主要目标是通过开发两种机器学习 (ML) 模型，即人工神经网络 (ANN) 和 4 倍交叉验证 (CV) 中的随机森林 (RF) 模型，准确地绘制容易发生沟壑侵蚀的区域。 ）。此外，我们使用多重共线性分析从 15 个条件因子中选择 11 个变量来训练 ML 模型进行沟蚀敏感性映射（GESM）。选择伊朗的拉默德县作为研究区域是因为拉默德县是该国受沟壑侵蚀影响最严重的地区之一。从 232 个沟壑样本中，75% 用于训练两个 ML 模型，其余样本 (25%) 用于使用 4 倍 CV 验证生成的 GEMS。RF 模型产生了更高的准确度，准确度值为 93%。RF 模型生成的 GEMS 显示，高度脆弱和非常脆弱的区域分别为 1,869 公顷和 5,148 公顷。两个模型的结果表明，由于植被覆盖率低，最脆弱的土地利用/土地覆盖类别是裸地。这项研究的结果可以帮助拉默德县的管理者通过采取预防措施来缓解水土流失问题并防止未来的沟渠侵蚀。RF 模型生成的 GEMS 显示，高度脆弱和非常脆弱的区域分别为 1,869 公顷和 5,148 公顷。两个模型的结果表明，由于植被覆盖率低，最脆弱的土地利用/土地覆盖类别是裸地。这项研究的结果可以帮助拉默德县的管理者通过采取预防措施来缓解水土流失问题并防止未来的沟渠侵蚀。RF 模型生成的 GEMS 显示，高度脆弱和非常脆弱的区域分别为 1,869 公顷和 5,148 公顷。两个模型的结果表明，由于植被覆盖率低，最脆弱的土地利用/土地覆盖类别是裸地。这项研究的结果可以帮助拉默德县的管理者通过采取预防措施来缓解水土流失问题并防止未来的沟渠侵蚀。