Improving groundwater nitrate concentration prediction using local ensemble of machine learning models

Prediction and optimization of wastewater treatment process effluent chemical oxygen demand and energy consumption based on typical ensemble learning models

Pollution integration and carbon reduction has become a primary focus in wastewater treatment processes. In this study, water quality and control indicators were used as input features and the dataset was extended using the moving average method. Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine algorithms were used to predict the effluent chemical oxygen demand (COD) and total energy consumption (TEC). The results indicated that the model prediction performance could be effectively improved when the data were amplified by two times and that the XGBoost model exhibited the best prediction performance for effluent COD and TEC. The Non-dominated Sorting Genetic Algorithm II model was employed for the multi-objective optimization of effluent COD and TEC, resulting in reductions of 15% and 18%, respectively. The ensemble learning model proposed in this study to achieve synergy between water quality improvement and energy saving is practical.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.