Abstract
This study focuses on the dielectric permittivity of Cherang Hangus soil, a laterite type known for its water retention and landfill liner applications. By leveraging GPR data alongside empirical models, researchers aim to understand how moisture content influences dielectric permittivity, which is critical for managing wastewater migration in various soil types. The methodology involves simulating landfill conditions using a concrete tank filled with Cherang Hangus soil and solid waste, allowing for detailed monitoring of moisture and GPR scans. The study employs various regression models to predict dielectric permittivity based on moisture content, including linear, logarithmic, and polynomial approaches. Results indicate that more complex models, particularly the third-order polynomial, provide better accuracy in capturing the relationship between moisture content and dielectric permittivity, as evidenced by high R² 0.9883 for 3rd order regression model for Cherang Hangus. The findings underscore the significance of soil composition in predicting dielectric permittivity, highlighting that the high gravel content in Cherang Hangus soil affects moisture retention and, consequently, the accuracy of empirical models.
Published Version
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