## 4 Bigram and Traditional Results
### Advancements in Machine Learning for Water Contaminants and Soil Erosion
#### 4.1.2 Traditional Analysis
In [36], the researchers employ HyMap-C airborne hyperspectral imagery and a machine learning ensemble technique based on a stacking strategy to estimate soil heavy metal concentrations in Yitong County, China. Using competitive adaptive reweighted sampling (CARS) to select spectral features, the CARS-Stacking method outperforms other methods in predicting arsenic, chromium, lead, and zinc concentrations, with determination coefficients ranging from 0.60 to 0.73. The estimated results closely align with the distribution trends of actual ground measurements. Machine learning models for soil erosion susceptibility mapping were proposed in [37]. The techniques employed include weighted subspace Random Forest, Gaussian process with radial basis function kernel, and naive Bayes. Critical factors were identified using simulated annealing feature selection, and the models' performances were evaluated based on accuracy, kappa coefficient, and probability of detection. The weighted subspace Random Forest method demonstrated superior performance, providing valuable tools for sustainable management, watershed conservation, and reduction in soil and water loss. In [38], uses high-spatial resolution arsenic (As) field observations from 3 million groundwater sources across India to identify regions with elevated groundwater As levels and their influencing factors. Statistical and machine learning methods model As concentrations and predict high As-hazard zones. Factors like geology-tectonics, groundwater-fed irrigated areas, and elevation influence As occurrence. The model's accuracy is 82-84% and AUC is 0.89-0.88. Around 90 million people are exposed to high groundwater As, but considering the modeled hazard, it could be over 250 million. In the Ganges River delta, the study [39] utilizes statistical methods and AI techniques, including Random Forest, Boosted Regression Trees, and Logistic Regression, to model the probability of groundwater arsenic concentrations surpassing WHO guidelines. A \"hybrid multi-modeling approach\" is employed, incorporating hydrostratigraphic parameters. The Random Forest model demonstrates superior performance, with 30.3 million people at risk from elevated groundwater As levels. These models offer valuable insights for identifying regional As-hazard zones and potential exogenous factors contributing to this significant natural pollution problem. Machine learning techniques like Random Forest and quantile Random Forest (QRF) are used to estimate redox conditions and nitrate concentrations in groundwater across Germany at a 1 km \(\times\) 1 km resolution [40]. Using the European Water Framework Directive monitoring network and spatial environmental information, the Random Forest model achieves a strong predictive performance with an R2 of 0.52. Dominant predictors include redox conditions, hydrogeological units, and arable land percentage. Uncertainty assessment with QRF reveals considerable uncertainties, but the study represents the first nationwide data-driven assessment of groundwater nitrate concentrations in Germany. The authors employ Random Forests Regression (RFR) to estimate stream nitrogen and phosphorus concentrations from sensor data in [41], addressing the limitations of linear regression models. RFR shows promise for most nutrient variations, with soil moisture and fluorescent dissolved organic matter as important predictors. This approach offers the potential to supplement traditional nutrient datasets. In [43], the researchers utilized 22,000 private well nitrate measurements from North Carolina to generate exposure estimates for unmonitored areas. They assessed multiple machine learning models and found that the final Random Forest model achieved a 0.75 accuracy. This model's predictions will be incorporated into the Agricultural Health Study cohort to estimate nitrate levels in private wells throughout unmonitored regions of North Carolina.