Hybrid Stochastic Genetic Evolution-Based Prediction Model of Received Input Voltage for Underground Imaging Applications

Abstract

The capacitive resistivity technique in underground object detection comprises configured transmitter and receiver antennas that are capacitively coupled to the ground. However, underground imaging lacks a basis for determining the received voltage for precise data analysis. This study aimed to develop a prediction model of the received input voltage signal amplitude from the ground of a single-pair antenna underground imaging system. The receiver antenna circuit for this application is designed and simulated in Proteus Software. Genetic Programming (GP) is applied to predict the received input signal based on the shape of the received waveform signal, operating frequency, resistance of the waveform shaping circuit, and buffer amplifier output signal. The resulting fitness function of GP (4) is acceptable as it scored an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 99.38% with a negligible MSE of 0.0059 and an MAE of 12.3423. Then, the GP fitness function is optimized through Genetic Algorithm (GA), Differential Evolution (DE), and Evolutionary Strategy (ES) in which the GP-GA model outperformed the two hybrid models providing fast convergence and 2.49e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−8</sup> best fitness value. This study proved that GP can be effectively combined with stochastic genetic evolution algorithms to avoid lengthy mathematical calculations and accurately estimate the natural voltage received from the ground.