Electrical resistivity tomography (ERT) has been seen as an appropriate instrument in several works to monitor and aid in the control of seawater intrusion (SWI) in coastal groundwater systems. This study seeks to discuss the synthesis of a digital twin that couples information between the physical space through ERT as a monitoring sensor and the digital space using SWI simulations to accurately model the behavior of SWI in the present and future settings. To showcase the concept, a Python-based simulation was presented that shows (a) the joint forward modeling-simulation scheme for calculating expected ERT apparent resistivity values from simulated SWI and (b) the calibration of the digital coastal aquifer system through genetic algorithm to accurately match the outputs of the SWI simulations with the ERT measurements.

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Root studies like propagation and morphological traits unlock a higher-level understanding of plants to better the growth of agricultural crops and maximize farm yields. As they are located underground, there is an additional challenge in elucidating their structure and behavior. Root imaging allows for real-time observations and there are several known methods. One imaging dataset simulated entire 3D root systems to create 2D images for data analysis and measurements. In this study, the dataset’s extracted measurements will be used to reconstruct the true parameters of the simulated 3D root system through the use of multigene symbolic regression genetic programming (MSRGP). Eleven (11) parameters were selected as the output variables, each for monocot and dicot data for a total of 22 MSRGP models. Among these, thirteen of them showed high R2 values greater than 80%, proving the high accuracy of the MSRGP method. Input variables that were frequently used across multiple models were also noted, such as tip count, exploration ratio, and area. In addition to the accuracy that MSRGP provides in predicting variables, the computation time of these models is found to be as low as a few milliseconds. Once trained, this speed allows the models to be integrated into relevant applications without significant increases in computation cost.

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