Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches

Recently, due to global climate change and population growth, environmental protection has become more interested. Water is the main critical issue because it is the most significant environmental resource. Therefore, this study introduces a novel approach to examine, modeling, and addressing the monitoring of water quality (WQ) critical scenario related to unexpected extreme variations of crucial indicators (UEVCI). Therefore, this research integrates ensemble machine learning (EML) techniques with Non-linear programming (NLP) and Simulated annealing algorithm (SAA) to develop an optimal weighted ensemble models. New development models were nonlinear-programmed ensemble machine learning (NLEML) and simulated annealing ensemble machine learning (SAEML). Besides, we developed least-squared boosted regression tree (LsBRT), artificial neural network (ANN), and multiple linear regression (MLR) models individually to compare the performance of new ensemble models. The South Platte River Basin in Colorado, USA was the study region. The initial dataset was extracted through the United States Geologic Survey (USGS) from 2023 to 2024. Preprocessing approaches such as cleaning missing data (CMD), cleaning outlier data (COD), and k-fold cross validation (KFCV) with k = 5 were used to prepare the dataset. The final dataset was utilized to examine variations of essential parameters that affect water health and quality, including the power of hydrogen (pH) and dissolved oxygen (DO). The results showed that the NLEML provided the most accurate results in estimating fluctuation of pH parameter with an R² coefficient of 0.85. Also, the NLEML estimated the variance of the DO parameter with an R² equal to of 0.79, resulting in an outperforming simulation.