## 4 Bigram and Traditional Results
### Advancements in Machine Learning for Water Contaminants and Soil Erosion
#### 4.1.2 Traditional Analysis
\begin{table}
\begin{tabular}{l l l l} \hline
**Techniques** & **Metrics** & **Application** \\ \hline MLR, CAMF, RF & Max, RMSE, R2: RF was the best & Groundwater database:CrushWiT to record groundwater & The spatial distribution of nitrate concentration \\ MLR, CAMF, RF & with R2 = 054, MAE = 59, RMSE = 9.19 & quality throughout the state of Hesse & in groundwater [34]. \\ \hline \multirow{3}{*}{SVM, RF} & MAE, RMSE, AUC: The best performance & Digital map of the spatial distribution of gilles. & Create gully erosion susceptibility map (GISM) \\  & was RF with AUC = 924, RMSE = 20874, & 130 locations of Eighth Dam Basin, in part of Eightham Dam Basin, Hameleh, \\  & MAE = 0.082 & Hamelength, western.Inn & western Iran [15]. \\ \hline SAM, RF, XGBout, & R2, RMSE, MAE of the tested models, & The collection of surface supplies was essentially & The prediction of four heavy metals is studied. \\ Extraflex, Adaboost, & X3600 defined sheet heat results & synchronized with the acquisition of airborne & Arenici (Ax), Graham (C), lead (Pb), \\ ELM, MLP & for all the analyzed metals. & hyperspectral data from late April and early May of 2017. & and after (Ga) [36]. \\ \hline RF, NB & Accuracy, Kappa, FOO, TD-Net model & Data included 227 samples of erosion and non-erosion & Machine learning models for mapping \\ Gaussian process & Kappas = 82\%, FOO = 94\% & Isometrics through field servers & susceptibility to soil erosion by water [27]. \\ \hline \multirow{3}{*}{RF} & \multirow{3}{*}{Accuracy, 82-84\%, AUC, 88-89\%,} & The data acquired from the National Rural Derihing & The data was acquired from the National Rural Derihing \\  & & Water Programme (RRDWP) and the Central Geomd & Use field observations of amenic (As) in groundwater \\  & & Water bands (COWN), best name the Ministry of rail with high-spatial resolution, with the aim of delineating & Shakit of the Government of India. In Indonesia \\  & & 2611,265 drinking water with (NRDW2018) and concentrations in groundwater [8]. \\  & & 694 monitoring results (COWN, 2018). & \\ \hline RF, Boosted Regression & Accuracy, Sensitivity, Specificity-The best & A total of 107,388 amenic data in groundwater were & \\ Logistic Regression (LR) and & model was Random Forest with 82\%, & Combined with four decades and published reports & Transboundary regional scale models are used to \\  & in all indicators. & from Hads and Barles/Rob. MS/DPE (2001), & calculate the probability of arsenic concentrations \\  & & & \\ \hline \multirow{3}{*}{RF, QRF} & \multirow{3}{*}{R2.05 (RFO), 024 (RFR), 051 (\(RFN_{0}\))} & The study uses from the WPD groundwater & \\  & & monitoring network in Germany and selects & Applus machine learning techniques to estimate \\  & & monitoring sites based on criteria like metadata, & groundwater redox conditions and nitrate \\  & & sampling depth, observation period, and & concentrations across Germany [40]. \\  & & excluding concentration outliers. & \\ \hline \multirow{3}{*}{RF} & MAE, RMSE, For \(N_{0}\), MAE = 0.075, & The dataset features nutrient parameters, method & \multirow{3}{*}{To estimate stream nitrogen (N) and phosphorus (P) concentrations from sensor data in a focused,} \\  & RMSE = 0.12 & & intervals ranging from 1 to 15 min, with \\  & & monthly quality levels for water quality monitoring. & \\ \hline \multirow{3}{*}{Keying SVM, GBM, Balancing class} & \multirow{3}{*}{Accuracy, Kappa, The best result with \multirow{3}{*}{Ranning, class}} & \multirow{3}{*}{Constrict \& \(\sim\) 22,99 groundwater nitrate measurements} \\  & & & from previous wells, North Carolina, collected and \\  & & & immediately by the X-OHES between 1970 and 2011. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & The data were obtained by [PERSON] et al. (2014) [21] & \\ \cline{1-1}  & & and used for modeling. & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of applications and techniques in Water Contaminants and Soil Enrosion.