Automated Particle Tracing & Sensitivity Analysis for Residence Time in a Saturated Subsurface Media

Abstract

Residence time of water flow is an important factor in subsurface media to determine the fate of environmental toxins and the metabolic rates in the ecotone between the surface stream and groundwater. Both numerical and lab-based experimentation can be used to estimate the residence time. However, due to high variability in material composition in subsurface media, a pragmatic model set up in the laboratory to trace particles is strenuous. Nevertheless, the selection and inclusion of input parameters, execution of the simulation, and generation of results as well as post-processing of the outcomes of a simulation take a considerable amount of time. To address these challenges, an automated particle tracing method is developed where the numerical model, i.e., flow and reactive transport code, MIN3P, and MATLAB code for tracing particles in saturated porous media, is used. A rectangular model domain is set up considering a fully saturated subsurface media under steady-state conditions in MIN3P. Streamlines and residence times of the particles are computed with a variety of seeding locations covering the whole model surface. Sensitivity analysis for residence time is performed over the varying spatial discretization and computational time steps. Moreover, a comparative study of the outcomes with Paraview is undertaken to validate the automated model (R2 = 0.997). The outcome of the automated process illustrates that the computed residence times are highly dependent on the accuracy of the integration method, the value of the computational time step, ∆t, spatial discretization, stopping criterion for the integration process of streamlines, location, and amount of seed points. The automated process can be highly beneficial in obtaining insights into subsurface flow dynamics with high variability in the model setup instead of laboratory-based experimentation in a computationally efficient manner.

Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network

“…The selected SW quantity variables are discharge and water level of the stream is highly influential on the overbank flooding in the surrounding area, demand of water supply, and fluvial ecology. Aquatic life is impacted significantly due to the temporal dynamics between the seasons of the stream discharge and water level [13][14][15][16][17]. Surface water quality Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

“…Park and Bae [43,44] develop a housing price forecasting model based on AI and ML algorithms such as Naïve Bayesian, and AdaBoost while comparing them to the classification accuracy performance. Ho et al [45] compared three ML methods of SVM, RF, and GBM on housing prices over a period of 18 years considering three error metrics of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) [46][47][48][49][50].…”

Demystifying the Preventive Measures for Flooding from Groundwater Triggered by the Rise in Adjacent River Stage

“…Groundwater flooding may initially be invisible as underground flooding. Flooded basements are an early sign of groundwater flooding [9][10]. As the water level rises the water may emerge above the ground level causing flooding of buildings, roads and farmland [11][12][13].…”