In order to manage soil salinity effectively, it is necessary to understand the origin and the spatial distribution of salinity. There are about 120 salt dome outcrops in southern Iran and little is known about their contribution as the potential sources of salts and the spatial pattern of salts around them. Six machine learning algorithms were applied to model topsoil electrical conductivity (EC) and sodium adsorption ratio (SAR) in the Darab Plain (surrounded by six salt domes), Fars Province. Decision trees (DT), k‐nearest neighbours (kNN), support vector machines (SVM), Cubist, random forests (RF) and extreme gradient boosting (XGBoost) were used as primary models and the Granger–Ramanathan (GR) method was used to combine the predictions of these models. The results showed that remotely sensed data contributed more to predict EC and SAR than terrain‐based data. In terms of root mean square errors (RMSE), Cubist followed by the RF model, tended to give the best estimates of EC, whereas for SAR, RF performed best and was followed closely by SVM and Cubist. Compared to the primary models, the GR method on average resulted in a decrease of 6.1% and 3.9% in RMSE and an increase of 10% and 10.9% in R² for EC and SAR, respectively. The spatial pattern of SAR and EC suggested that the contribution of salt domes in soil salinization varied significantly according to their hydraulic behaviour in relation to adjacent aquifers and their activity. In general, the model averaging approach showed the potential to improve the estimates of EC and SAR.

中文翻译：

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伊朗南部与盐丘相关的土壤盐分：平均机器学习模型的预测和制图

为了有效管理土壤盐分，有必要了解盐分的起源和空间分布。伊朗南部大约有120个盐丘露头，人们对其盐的潜在来源及其周围盐的空间格局知之甚少。应用了六种机器学习算法来模拟Fars省达拉布平原（被六个盐穹顶环绕）的表层土壤电导率（EC）和钠吸附率（SAR）。决策树（DT），k最近邻（kNN），支持向量机（SVM），立体派，随机森林（RF）和极端梯度增强（XGBoost）被用作主要模型，而Granger–Ramanathan（GR）方法用于组合这些模型的预测。结果表明，与基于地形的数据相比，遥感数据对预测EC和SAR的贡献更大。就均方根误差（RMSE）而言，Cubist和RF模型倾向于给出EC的最佳估计，而对于SAR，RF表现最好，而SVM和Cubist紧随其​​后。与主要模型相比，GR方法平均导致RMSE下降6.1％和3.9％，而RMSE则分别增长10％和10.9％R ²分别用于EC和SAR。SAR和EC的空间格局表明，盐丘在土壤盐渍化中的贡献根据其相对于相邻含水层的水力行为及其活动而显着变化。通常，模型平均方法显示了改进EC和SAR估算的潜力。