## 2 Literature Review
### Machine Learning Techniques for Water Quality  
[PERSON] et al. [13] used several popular ML models, like group method of data handling (GMDH), a support vector machine (SVM), and an artificial neural network (ANN), as well as artificial intelligence techniques, to predict the water quality of the Tiresh River (Iran). They measured some essential parameters (Ca, Cl, Mg, Hco3, pH, So4, Ec, TDS, Na, etc.) by which the water quality could be predicted. After training and testing, they stated which model was most suitable for which parameters among the three models. The SVM's accuracy was higher than that of the others (GMDH and ANN) for the parameters Ca, Cl, Na, and So4. For Mg and Hco3, both the SVM and ANN's performance was better than that of GMDH. And for Ec and TDS, all three models had an equally suitable performance. The SVM had the best performance in terms of pH estimation, but its accuracy was slightly lower than the standard. In another study, [PERSON] et al. [21] demonstrated that combining multiple artificial neural networks provides better WQI than a single feedforward neural network. Using bothforward selection and backward elimination selective combination methods, they obtained coefficient of determination (R\({}^{2}\)) and mean squared error (MSE) values of 0.9340 and 0.9270 and 0.1156 and 0.1256, respectively. They used a total of 25 parameters for the model input, excluding biological oxygen demand (BOD) and chemical oxygen demand (COD). Similar to this research, [PERSON] et al. [14] estimated water quality and identified the key parameters for water quality prediction. They stated that not only the ML models but also some key parameters of the water dataset are responsible for accurate predictions. They used a total of 10 learning models for water quality prediction on a big dataset. Of these, the decision tree (DT), RF, and deep cascade forest (DCF) models showed better performance with two key parameter sets (DO, CODMn, and NH3-N; CODMn and NH3-N). Authors like [PERSON] et al. [22] estimated WQI according to water quality class (WQC) more quickly and inexpensively. Using only four parameters, they determined that gradient boosting, with a learning rate of 0.1, and polynomial regression, with a degree of 2, made predictions the most efficiently for determining WQI among all of the models, with mean absolute error (MAE) values of 1.9642 and 2.7273, respectively. For predicting WQC, a multi-layer perceptron (MLP) performed more accurately, with a configuration of \((3,7)\) and with an accuracy of 0.8507. Researchers observed a strong nonlinear relation between estuarine NH4\({}^{+}\)-N and NH4\({}^{+}\)-N in the upper reaches when predicting NH4\({}^{+}\)-N in the Xiaoqing River estuary, China. [PERSON] et al. [23] used several ML models, such as ANNs, RF, and XGB, to validate their observations. An additive explanation method was used to format the XGB model. This study revealed that when the NH4\({}^{+}\)-N concentration in the upper reaches is less than 2 mg/L, then there is no negative impact on estuarine NH4\({}^{+}\)-N.  
For surface water quality prediction, a data-driven model was developed by [PERSON] et al. [24] to account for water quality variations, and real-time early warnings were their primary concerns. This focused model was developed using adjusted integration of an improved genetic algorithm (IGA) at first and then a backpropagation neural network (BPNN). The developed model was applied to the Ashi River, China, and compared with a simple BPNN model. It was observed that in terms of accuracy, reliability, and the provision of real-time early warnings, in every case, the data-driven model performed better. The authors in [16] used five learning models to determine the biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. A total of eleven parameters were considered within a period of ten years. A comparison between specific and integrative models with two feature extraction algorithms, a genetic algorithm (GA) and Principal Component Analysis (PCA), was shown.