The performance of four tree-based classification techniques—classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths—an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.

中文翻译：

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预测德克萨斯州奥加拉拉含水层硝酸盐过量的基于树的建模方法

将四种基于树的分类技术——分类和回归树 (CART)、多元自适应回归样条 (MARS)、随机森林 (RF) 和梯度提升树 (GBT) 的性能与常用的逻辑回归 (LR) 进行比较评估德克萨斯州奥加拉拉含水层含水层脆弱性的分析。结果表明，基于树的模型比逻辑回归模型表现更好，因为它们能够局部改进硝酸盐超标概率。RF 表现出最好的泛化能力。CART 模型在预测非超标方面做得更好。硝酸盐超标对井深很敏感——这是含水层氧化还原条件的一个指标，而这又受碳酸钙溶解引起的碱度增加的控制。土壤的粘土含量和土壤有机质作为农业活动的指标，也被指出对硝酸盐超标有显着影响。研究区东北部有限的动物饲养场操作可能释放的氮似乎也很重要。综合土壤、水文地质和地球化学数据集，结合基于树的方法，有助于阐明控制硝酸盐超标的过程。总体而言，基于树的模型提供了灵活、透明的方法来绘制硝酸盐超标、确定潜在机制和确定监测活动的优先级。研究区东北部有限的动物饲养场操作可能释放的氮似乎也很重要。综合土壤、水文地质和地球化学数据集，结合基于树的方法，有助于阐明控制硝酸盐超标的过程。总体而言，基于树的模型提供了灵活、透明的方法来绘制硝酸盐超标、确定潜在机制和确定监测活动的优先级。研究区东北部有限的动物饲养场作业可能释放的氮似乎也很重要。综合土壤、水文地质和地球化学数据集，结合基于树的方法，有助于阐明控制硝酸盐超标的过程。总体而言，基于树的模型提供了灵活、透明的方法来绘制硝酸盐超标、确定潜在机制和确定监测活动的优先级。