## 4 Bigram and Traditional Results
### Advancements in Machine Learning for Water Contaminants and Soil Erosion
#### 4.1.2 Traditional Analysis
Random Forest (RF), multi-layer perception (MLP), Classification and Regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and MLR (multiple linear regression). \begin{table}
\begin{tabular}{l l l l l} \hline
**Techniques** & **Metrics** & **Data** & **Application** \\ \hline \multicolumn{5}{c}{70 variables were retained in the final} \\  & model, with an R2 of 0.83 for the & The model uses data from 12,082 wells and & The objective of this study is to develop an extreme variation product variable, providing accurate estimates at both national and regional scales, & gradient boosting (CGB) machine learning model \\ XGB & \multicolumn{5}{c}{} \\  & training data and 0.40 for the 1080 output & The task used in the study consist of over 6000 groundwater & To use machine learning methods, specifically random measurements of language (Mi) & To use and generalized boosted regression modeling, to analyze over 600 groundwater measurements of 0.80 and a Cohen’s Kappa score of 0.43, with other physicochemical parameters. & In Southeast Asia and Bangladesh, \\ \hline Gaussian mixture model & The observed first model yielded 10.82% in R2.185.57 & Converting global number to normalized reference & To present a framework called cluster-based machine learning model \\ clustering technique, random forest regressor (RF) & In RSNED, 30.97% in MAPE, and 10.81 & values, and switching the processed images with the & having prognostic for quicked training (CMO) \\  & in TSN compared to the handbreaking case. & Superposed Sediment Concentration (SSC) dataset & spring of suspected sediment in human injuries [46]. \\ \hline \multicolumn{5}{c}{The results show that Random Forest} \\ Random Forest Classification & Classification achieves a 66% testing accuracy & The study employs four NDEP datasets to analyze & To develop Random Forest Classification and the \\ and Regression & The model’s Forest Regression yields an R2 & factors impacting arsenic concentrations in private wells & Rejects, to identify factors contributing to higher arsenic concentration in private \\  & & orchards, contaminated sites, and abandoned misses in & drinking market wells in west-central \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline MLP: a customized CNN, & Customized CNN model achieved an F1 & The study used IRS dataset from EU/UNDF EMBALS II & The study aims to develop an open-source end-to-end & The \\  & score of 0.93% and identified the & project (2016-2017), Svensar samples analyzed via the & workflow to estimate pollution and from many \\  & majority of chemical compounds from & L-CHRRS, resulting in 0.849 signals, 35 compounds & having progress or other unidentified sources using a deep learning convolutional neural network \\  & the Durable New. & tentatively identified using two target screening workflow. & classification model [48]. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._A GIS-based statistical approach to estimate groundwater nitrate concentration was developed in [34], addressing the limitations of complex hydro-biogeochemical models. Multiple linear regression, Classification and Regression Trees, Random Forest, and Boosted Regression Trees are compared for predictive performance. The Random Forest model performs best, identifying hydrogeological units, arable land percentage, and nitrogen balance as key predictors. This method advances regional-scale groundwater nitrate prediction using only spatially available data. In [35], the authors compare four machine learning models for creating gully erosion susceptibility maps in western Iran, taking into account variables like distance from rivers, calcium carbonate equivalent, and topographic position index. Random Forest outperforms other models with the highest predictive performance (92.4% mean AUC). All models show stable performance through 10-fold cross-validation. The generated maps can help identify erosion risk areas, informing soil conservation and sustainable management plans by considering crucial contributing factors.