Predicting conductance due to upconing using neural networks.

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.

Mobilization of natural colloids from an iron oxide-coated sand aquifer: effect of pH and ionic strength.

Field and laboratory column experiments were performed to assess the effect of elevated pH and reduced ionic strength on the mobilization of natural colloids in a ferric oxyhydroxide-coated aquifer sediment. The field experiments were conducted as natural gradient injections of groundwater amended by sodium hydroxide additions. The laboratory experiments were conducted in columns of undisturbed, oriented sediments and disturbed, disoriented sediments. In the field, the breakthrough of released colloids coincided with the pH pulse breakthrough and lagged the bromide tracer breakthrough. The breakthrough behavior suggested that the progress of the elevated pH front controlled the transport of the mobilized colloids. In the laboratory, about twice as much colloid release occurred in the disturbed sediments as in the undisturbed sediments. The field and laboratory experiments both showed that the total mass of colloid release increased with increasing pH until the concurrent increase in ionic strength limited release. A decrease in ionic strength did not mobilize significant amounts of colloids in the field. The amount of colloids released normalized to the mass of the sediments was similar for the field and the undisturbed laboratory experiments.