# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Machine Learning Paradigm
Environmental studies, particularly those addressing the fate of pollutants, often rely on time-series data to make predictions based on observed trends. It is widely acknowledged among machine learning practitioners that accurate predictions derived from time-series data are immensely valuable. The reliability of these predictions hinges on factors such as the quality and size of the time series dataset, as well as the time horizon for which the prediction is needed; typically, short-term predictions entail lower uncertainties [509]. Deep learning techniques and time series analysis methods encompass Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models [510; 511; 512]. In recent years, machine learning has also been adapted for water science studies, with many publications focusing on the applicability of algorithms for modelling water quality in different environments [513; 514; 515; 516; 517; 518; 519; 520] and water treatment [521; 522; 523; 524; 525; 526]. Machine learning methods have also been successfully applied in the identification, tracking, and removal of pollutants [527; 528; 529; 530; 531; 532; 533]. In the context of the numerous studies on water quality, machine learning can predict the amount and fate of pollutants, taking into account complex processes and interactions between different control parameters. Particular attention is paid to organic pollutants and the assessment of contaminated sites, including through the application of image recognition technology [534; 535; 536; 537; 538; 539]. Sprocati and Rolle [535] focused on the electrokinetically assisted bioremediation (EK-Bio) of organic pollutants (chlorinated ethenes) in porous media with low permeability. They simulated the complex processes in a simplified horizontal 2D domain between a cathode-anode doublet. Reactive transport was simulated using the code NP-Phreeqc-EK [540], which combines the flow and transport software COMSOL Multiphysics with the geochemical code PHREEQCRM, which performs seven kinetically controlled reactions and six equilibrium reaction calculations. The surrogate model approach, a neural network utilising a stacked MLP (multilayer perceptron), was then adapted to investigate the response of the system for different input combinations. Xia et al. [538] used Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) to predict DCE degradation at the contaminated site. The variables from 3 months of data were used for training and prediction of DCE concentrations. This means that the input variables for the models were the data from one sampling process, and the prediction was made for the next sampling process. The performance of the model was evaluated using three common assessment measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). XGBoost was more accurate at high DCE concentrations, and reproduced DCE variations better. Chen et al. [539] combined numerical models such as WOFOST, HYDRUS, and MODFLOW with a machine learning model. In this application example, the saturated zone flow model MODFLOW is replaced by a neural network with radial basis function, which re duces the computational cost, but does not reduce the accuracy. The paradigm is validated using an eco-hydrological model that couples WOFOST and an unsaturated flow model HYDRUS. This solution requires real data (meteorological, soil, and crop data), and the observed data can also be used as a training set for an RBF neural network. This paradigm could later be further developed by replacing two physically based numerical methods, i.e., WOFOST and HYDRUS, with machine learning models.