Estimation of EM Field Penetration Depth in Soils using Cascade PNN-RBF Model

In this paper, a cascade PNN-RBF neural model was developed to rapidly estimate the ground penetration depth of EM (electromagnetic) waves in the frequency range of 100-700 MHz. The input parameters of this model are soil composition (clay, silt and sand content), soil moisture and frequency. The model consists of a PNN network that determines the type of soil, pairs of RBF networks that determine the real and imaginary components of the relative permittivity of the soil based on the type of soil and its moisture, and function modules that estimate the penetration depth of EM waves based on the values of the complex relative permittivity and frequency. The effectiveness of this model is demonstrated by estimating the penetration depth of EM waves in clay loam soil.