## 4 Bigram and Traditional Results
### Assessing Water Quality and Potability
#### 4.2.2 Traditional Analysis
\begin{table}
\begin{tabular}{l l l l} \hline
**Techniques** & **Metrics** & **Data** & **Application** \\ \hline DT, Naive Bayes & Accuracy, 97.23\%, & The dataset for the study was adopted & To compare the performance of two machine learning algorithms—the Decision Tree Algorithm and the Naive Bayes algorithm—in predicting drinking water quality [8]. \\ \hline \multirow{4}{*}{RF} & MAE, RMSE, R2. The best R2 was achieved & \\  & in 2010, with a value of 0.93, an MAE of 0.132, & Remote sensing and GIS methods were & To introduce a study that uses a Random Forest model to predict shallow groundwater nitrate concentrations in the Yuchuan Region of central \\  & and an RMSE of 0.220, with a fixed 0.932, an MAE & Employed to create training and test sets & Yuchuan Plan during four different years [59]. \\ \hline \multirow{4}{*}{RNN} & the ANN model demonstrated the best performance & \\  & for scenario 1, with R2 value above 0.97 \# full full & Observations were collected from wells & To develop accurate and reliable machine learning models for predicting within the basin, utilizing 140 water & irrigation water quality parameters, which can help plan irrigation water and crop management more effectively [60]. \\ \cline{1-1}  & variables (RSS, CA, SR, R1, SR, and KD) & & \\ \cline{1-1}  & training and testing, The MLK models showed better & \\ \cline{1-1}  & results in scenario 2 compared to the ANN and & \\ \cline{1-1}  & LSTM models. & & \\ \hline \multirow{4}{*}{ANN} & MAE, RMSE, R2. On average, across the various experiments, R2 achieved a value of 0.83, MAE & Data collected from a high-resolution & Introduces proximal remote sensing for inland water quality monitoring, presents a high-resolution hyperspectral image, and demonstrates the 11 m capabilities continues & effectiveness of machine learning algorithms in accurately estimating key observation and effective long-term & water quality parameters such as nitrogen, phosphorus, and chemical oxygen using this new technology [61]. \\ \cline{1-1}  & value of 0.39, & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{4}{*}{Parsen CC-SVM} & The AUC of the PCC5/SVM-based method & Taking the dataset into consideration, the parameters included residual chlorine, pH, turbidity, temperature, conductivity, oxidative-reduction potential, and chemical oxygen demand. & To emphasize the increasing risk of cross-connections in portable-reculined water that distribution systems, and highlight the need for reliable, cost-effective, and real-time online detection methods [52]. \\ \cline{1-1}  & & The article compares the advantages of various remote sensing platforms and inversion models & comparing various remote sensing platforms, inversion models, and water quality parameters & To provide an overview of the development and current applications of hyperspectral remote sensing in inland water quality detection. \\ \cline{1-1}  & informsering & & \\ \cline{1-1}  & applications for multiple water quality parameters. & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._A data-driven water classification model for rural household areas using sensor nodes and a machine learning algorithm was developed in [55] to classify water potability using a hard-voting method in ensemble learning. The results show that the voting classifier model achieves an accuracy of 97% compared with other stand-alone classification algorithms. In [64], another interesting approach used an edge-cloud ubiquitous sensor network for low-cost water quality measurement to supplement existing IoT-based infrastructure. Machine learning algorithms are applied to a dataset containing eight fields related to water potability. Then, 16 machine learning algorithms for potability prediction were compiled, including 11 shallow learning algorithms and 5 deep learning algorithms. CNN-Batch Normalization, the most accurate of these algorithms, achieved a maximum testing accuracy of 85.03%.