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Kiangala, S. K., & Wang, Z. (2021). An Effective Adaptive Customization Framework for Small Manufacturing Plants Using Extreme Gradient Boosting-XGBoost and Random Forest Ensemble Learning Algorithms in an Industry 4.0 Environment. Machine Learning with Applications, 4, Article ID: 100024.
https://doi.org/10.1016/j.mlwa.2021.100024

has been cited by the following article:

• TITLE: Modeling of Total Dissolved Solids (TDS) and Sodium Absorption Ratio (SAR) in the Edwards-Trinity Plateau and Ogallala Aquifers in the Midland-Odessa Region Using Random Forest Regression and eXtreme Gradient Boosting

  AUTHORS: Azuka I. Udeh, Osayamen J. Imarhiagbe, Erepamo J. Omietimi

  KEYWORDS: Water Quality Prediction, Predictive Modeling, Aquifers, Machine Learning Regression, eXtreme Gradient Boosting

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.12 No.5, May 30, 2024

  ABSTRACT: Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R2 values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.