Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9–5 parameters without losing their learned information over the models’ training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost–SVM, XGBoost–ANN, XGBoost–Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost–Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.

开发了混合人工智能（AI）模型来预测澳大利亚两个海湾（即布兰堡（BB）和欺骗（DB））站的沉积物铅（Pb）。提出了一种称为极端梯度增强（XGBoost）的特征选择（FS）算法，以抽象出相关的Pb预测输入参数，并针对分析的主成分（PCA），递归特征消除（RFE）和遗传算法（GA）进行了验证）。XGBoost模型使用网格搜索策略（Grid-XGBoost）来预测Pb，并针​​对常用的AI模型，人工神经网络（ANN）和支持向量机（SVM）进行了验证。输入参数选择方法将21个参数重新定义为9-5个参数，而不会在模型的训练阶段丢失其学习的信息。在BB站 XGBoost-SVM，XGBoost-ANN，XGBoost-Grid-XGBoost和Grid-XGBoost模型的平均绝对百分比误差（MAPE）值分别为（0.06、0.32、0.34和0.33）。在DB站，对于XGBoost-Grid-XGBoost和Grid-XGBoost模型，分别获得最低的MAPE值0.25和0.24。总体而言，提出的混合AI模型为沉积物铅的预测提供了可靠而强大的计算机辅助技术，有助于对环境污染监测和评估的最佳了解。