Abstract
One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models.