Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R2) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.

中文翻译：

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模拟地表水溶解氧的遥感和现场数据的功能化：基于混合树的人工智能模型的开发。

溶解氧 (DO) 是环境工程师和生态科学家了解河流健康状况的重要河流健康指标。本研究旨在评估四种特征选择器算法的可靠性，即 Boruta、遗传算法 (GA)、多元自适应回归样条 (MARS) 和极端梯度提升 (XGBoost) 以选择最合适的应用水质预测器 (WQ) ） 参数; 并比较四种基于树的预测模型，即随机森林 (RF)、条件随机森林 (cForest)、随机森林生成器 (Ranger) 和 XGBoost，以预测马来西亚巴生河中溶解氧 (DO) 的变化。总特征包括来自监测站点数据的15个WQ参数和来自遥感数据的7个水文分量。根据算法 XGBoost 和 MARS 在应用统计评估器方面选择的特征，所有预测模型都表现良好。此外，当 MARS 和 XGBoost 算法选择特征时，XGBoost 预测模型在所有应用的预测模型中表现最好，决定系数 (R2) 值分别为 0.84 和 0.85，但边际性能提高了Boruta-XGBoost 模型在这种情况下。