Susceptibility mapping of groundwater salinity using machine learning models.

Increasing groundwater salinity has recently raised severe environmental and health concerns around the world. Advancement of the novel methods for spatial salinity modeling and prediction would be essential for effective management of the resources and planning mitigation policies. The current research presents the application of machine learning (ML) models in groundwater salinity mapping based on the dichotomous predictions. The groundwater salinity is predicted using the essential factors (i.e., identified by the simulated annealing feature selection methodology) through k-fold cross-validation methodology. Six ML models, namely, flexible discriminant analysis (FDA), mixture discriminant analysis (MAD), boosted regression tree (BRT), multivariate adaptive regression spline (MARS), random forest (RF), support vector machine (SVM), were employed to groundwater salinity mapping. The results of the modeling indicated that the SVM model had superior performance than other models. Variables of soil order, groundwater withdrawal, precipitation, land use, and elevation had the most contribute to groundwater salinity mapping. Results highlighted that the southern parts of the region and some parts in the north, northeast, and west have a high groundwater salinity, in which these areas are mostly matched with soil order of Entisols, bareland areas, and low elevations.

Different modelling approaches for predicting titanium dioxide nanoparticles mobility in intact soil media.

Understanding the transport behaviour of new and emerging materials such as engineered nanoparticles (ENPs) is vital for the accurate assessment of their functionality and fate in environmental systems. Predicting ENP mobility in soil systems based on common attributes of either soil or ENPs is of significant interest as an alternative to conducting laborious and time consuming column experiments. Thus this study investigates the importance of different soil properties and experimental conditions on titanium dioxide nanoparticles (nTiO2) mobility in real soil media and also evaluates four different modelling approaches including Multiple Linear Regression (MLR), Classification and Regression Tree (CART), Random Forest (RF) and Artificial Neural Network (ANN) for predicting nTiO2 mobility in soil media. The performance of both ANN and RF models were good for predicting nTiO2 transport in soil media, with ANN predictions being slightly superior to RF with less generalization errors. However, RF had the advantage of requiring less input predictors. In comparison the MLR model exhibited poor performance in both calibration and validation datasets, and while the validity of CART was almost acceptable in the calibration dataset, its efficiency was poor for the validation dataset. In addition to soil solution chemistry and hydraulic properties, other important factors having a major contribution to nTiO2 transport through soil included soil fracture associated properties and the existence of preferential flows.