Computation of Permeability of Soil using Artificial Intelligence Approaches
Jitendra Khatti1, Kamaldeep Singh Grover2

1Jitendra Khatti*, PhD Fellow, Department of Civil Engineering, Rajasthan Technical University, Kota, (Rajasthan), India. 
2Kamaldeep Singh Grover, Professor, Department of Civil Engineering, Rajasthan Technical University, Kota, (Rajasthan), India. 
Manuscript received on October 20, 2021. | Revised Manuscript received on October 28, 2021. | Manuscript published on October 30, 2021. | PP: 257-266 | Volume-11 Issue-1, October 2021. |  Retrieval Number: 100.1/ijeat.A32201011121 | DOI: 10.35940/ijeat.A3220.1011121
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Abstract: The Gaussian Process Regression (GPR), Decision Tree (DT), Relevance Vector Machine (RVM), and Artificial Neural Network (ANN) AI approaches are constructed in MATLAB R2020a with different hyperparameters namely, kernel function, leaf size, backpropagation algorithms, number of neurons and hidden layers to compute the permeability of soil. The present study is carried out using 158 datasets of soil. The soil dataset consists of fine content (FC), sand content (SC), liquid limit (LL), specific gravity (SG), plasticity index (PI), maximum dry density (MDD) and optimum moisture content (OMC), permeability (K). Excluding the permeability of soil, rest of properties of soil is used as input parameters of the AI models. The best architectural and optimum performance models are identified by comparing the performance of the models. Based on the performance of the AI models, the NISEK_K_GPR, 10LF_K_DT, Poly_K_RVM, and GDANN_K_10H5 models have been identified as the best architectural AI models. The comparison of performance of the best architectural models, it is observed that the NISEK_K_GPR model outperformed the other best architectural AI models. In this study, it is also observed that GPR model is outperformed ANN models because of small dataset. The performance of NISEK_K_GPR model is compared with models available in literature and it is concluded that the GPR model has better performance and least prediction error than models available in literature study.
Keywords: Artificial Neural Network, Gaussian Process Regression, Optimum Performance, Relevance Vector Machine
Scope of the Article: Artificial Intelligence