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Artificial Intelligence Using FFNN Models for Computing Soil Complex Permittivity and Diesel Pollution Content

Hamsa Nimer, Rabah Ismail, Adnan Rawashdeh, Hashem Al-Mattarneh, Mohanad Khodier, Randa Hatamleh, Musab Abuaddous

Abstract


Soil pollution caused by hydrocarbons, such as diesel, poses significant risks to both human health and the ecosystem. The evaluation of soil pollution and various soil engineering applications often relies on the analysis of complex permittivity, encompassing parameters such as dielectric constant and dielectric loss. Various computational models, including theoretical physics-based models, mixture theory models, statistical empirical models, and artificial neural network (ANN) models, have been explored for computing soil complex permittivity and predicting water and pollutant content. Theoretical models require detailed data that is often unavailable, and thus have limited applicability. Mixture models tend to underestimate soil characteristics due to inaccuracies in permittivity estimation of soil phases. While empirical models are widely used, their applicability is restricted to specific soil types, datasets, and locations. ANN models offer promising predictions, accommodating nonlinear phenomena and allowing for missing information and variables. In this study, capacitive electromagnetic electrode sensors were utilized to determine the complex permittivity of soil contaminated with varying levels of diesel at different moisture levels. Theoretical mixture, empirical, and Feed Forward Neural Network (FFNN) models were employed to compute the permittivity of polluted soil based on its phases and to predict the level of diesel pollution. A comparison of these modeling approaches revealed that the FFNN model exhibited the best performance. The ANN model demonstrated superior performance metrics, including a high correlation coefficient and lower mean square error. Specifically, the correlation coefficients for the FFNN model were 0.9942 for training samples, 0.9967 for validation samples, and 0.9977 for test samples. Additionally, the ANN model yielded the lowest mean square error compared to the other three models.

 

Doi: 10.28991/CEJ-2024-010-09-018

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Keywords


Complex Permittivity; Dielectric Properties; Soil; Diesel Contamination; Water Content; Artificial Neural Network; Artificial Intelligence.

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DOI: 10.28991/CEJ-2024-010-09-018

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