Wastewater land application is a widely accepted solution for addressing global water crisis, particularly in arid and semiarid regions, but it may cause soil Ca, Mg, and Na accumulation and result in soil degradation. The objective of this study was to investigate the underlying mechanisms impacting soil Ca, Mg, and Na in wastewater land application systems using tree‐based machine learning models. Using data collected from previous field studies, decision tree (DT), random forest (RF), and extreme gradient boosting decision trees (XGBoost) models were developed to predict soil Ca, Mg, and Na in wastewater land application systems. XGBoost models showed the best performance, with R2 and RMSE values of 0.999 and 18.9 mg/kg, 0.999 and 3.2 mg/kg, and 0.912 and 104 mg/kg on the training data and 0.989 and 345 mg/kg, 0.925 and 56.1 mg/kg, and 0.908 and 112 mg/kg on the test data for soil Ca, Mg, and Na prediction, respectively. Permutation importance analysis reveals that initial soil Ca and electrical conductivity (EC) and total irrigation amount, initial soil Mg, total precipitation and initial soil EC, and initial soil Na, total irrigation amount and wastewater Na were the top three predictive variables for soil Ca, Mg, and Na, respectively. Partial dependence analysis demonstrates how soil Ca, Mg, and Na changed with the predictive variables, and indicates that wastewater irrigation caused soil Ca, Mg, and Na accumulation. This study highlights the need for sustainable wastewater land application management to control soil sodium adsorption ratio and mitigate the risks of land degradation.

中文翻译：

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使用机器学习模型研究废水土地应用系统中土壤 Ca、Mg 和 Na 的影响机制


废水用地是解决全球水危机的广泛接受的解决方案，尤其是在干旱和半干旱地区，但它可能会导致土壤 Ca、Mg 和 Na 积累并导致土壤退化。本研究的目的是使用基于树木的机器学习模型研究影响废水土地应用系统中土壤 Ca、Mg 和 Na 的潜在机制。利用从以前的实地研究中收集的数据，开发了决策树 （DT）、随机森林 （RF） 和极端梯度提升决策树 （XGBoost） 模型来预测废水土地应用系统中的土壤 Ca、Mg 和 Na。XGBoost 模型表现出最佳性能，训练数据的 R2 和 RMSE 值分别为 0.999 和 18.9 mg/kg、0.999 和 3.2 mg/kg、0.912 和 104 mg/kg，土壤钙的 0.989 和 345 mg/kg、0.925 和 56.1 mg/kg、0.908 和 112 mg/kg。 分别是 Mg 和 Na 预测。排列重要性分析表明，初始土壤 Ca 和电导率 （EC） 和总灌水量，初始土壤 Mg、总降水和初始土壤 EC，以及初始土壤 Na、总灌水量和废水 Na 分别是土壤 Ca、Mg 和 Na 的前三个预测变量。部分依赖性分析显示了土壤 Ca、Mg 和 Na 如何随预测变量而变化，并表明废水灌溉导致土壤 Ca、Mg 和 Na 积累。本研究强调了可持续废水土地应用管理的必要性，以控制土壤钠吸附率并减轻土地退化的风险。