Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

Abstract

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management.

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

“…The RMSE values serve as indicators of the model’s efficiency by assessing the agreement between calculated values and experimentally measured values. On the other hand, MBE values are employed to ascertain the standard deviation between the predicted and measured values [ 36 , 37 , 38 ]. These statistical parameters were calculated using equations [ 39 ].…”

Blockchain-Orchestrated Intelligent Water Treatment Plant Profiling Framework to Enhance Human Life Expectancy

“…In this analysis, they got the highest accuracy of 0.80 using the RF classifier. Later, [23] presented the comparison of individual supervised ML models, such as gene expression programming and artificial neural network, with that of an ensemble learning model, i.e., RF, for predicting river water salinity in terms of electrical conductivity and dissolved solids. In terms of accuracy, the RF model outperforms other models on the training and testing datasets, followed by gene expression programming and artificial neural network models.…”

“…The authors [21], [23], [25], [28] incorporated ML into their solution. In some of their research work, the authors have not focused on the water potability prediction and their effects on human life; rather, they have discussed how different components of water affect the quality of water, such as authors of [21] predicted the effluent concentration of total nitrogen a few hours ahead, authors of [28] predicted the input and effluent chemical oxygen demand, authors of [25] predicted sludge volume index and authors of [23] predicted water salinity. However, some of the researchers have not considered the effects of poor-quality water on human life expectancy and security aspect.…”

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

“…Several ML approaches have been promoted for modelling WQ parameters. The ML models applied include ANN [10,[26][27][28], adaptive neuro-fuzzy inference system (ANFIS) [7,29,30], (SVR) [31][32][33], random forest (RF) [34,35], k-nearest neighbours (KNN) [36], Naive Bayes [37], decision tree (DT) [38,39], and extreme gradient boosting (XGB) [40]. The advantages and disadvantages of the most used ML techniques are summarised in Table 2.…”