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Prediction of soil thermal conductivity based on Intelligent computing model

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Abstract

Thermal conductivity is a basic characteristic of the heat conduction properties of subsoil. Previous research shows that soil thermal conductivity has complex correlations with many soil physical parameters, such as dry density, water content, mineral composition and particle-size distribution. In this paper, several artificial intelligence calculation methods are used to study the soil heat conduction mechanism and establish predictive models of thermal conductivity: an artificial neural network (ANN), adaptive neural network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Their modelling performance was evaluated by several metrics: correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and variance account for (VAF). Monte Carlo simulation was used to verify the robustness of the models, and the results of traditional empirical relationship models are used for comparison. The ANN, ANFIS and SVM models can accurately predict soil thermal conductivity, with R2 > 0.89, RMSE < 0.22 (Wm−1 K−1), MAE < 0.14 (Wm−1 K−1) and VAF > 88%. The ANN model had the best predictive accuracy, with R2 = 0.9535, RMSE = 0.1338 (Wm−1 K−1), MAE = 0.0952 (Wm−1 K−1) and VAF = 95.25%. The SVM model had similar accuracy, while that of the ANFIS model was lower. Monte Carlo simulations show that the SVM model provided the most robust predictions and that all three models were significantly better than the traditional empirical models. The SVM model is suggested as the best model for predicting soil thermal conductivity.

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Abbreviations

ANN:

Artificial neural network

ANFIS:

Adaptive neural network-based fuzzy inference system

SVM:

Support vector machine

R 2 :

Correlation coefficient

RMSE :

Root mean square error

MAE :

Mean absolute error

VAF :

Variance account for

N 1, N 2, N k :

Input parameters

Y :

Output parameter

x max :

Maximum values

x min :

Minimum values

x :

Actual value

x norm :

Normalized value

λ dry :

Thermal conductivities of dry soil [Wm−1 K−1]

λ sat :

Thermal conductivities of saturated soil [Wm−1 K−1]

γ d :

Dry density [kgm−3]

n :

Porosity [%]

λ:

Thermal conductivity [Wm−1 K−1]

H k :

The predicted value

w x,ji :

Weighting factor

bx,j :

Bias factor at the jth hidden node

w y,j :

Weighting factor for the jth hidden node

b y :

Bias factor in the output layer

M :

Number of parameters evaluated by the same regression process

N :

Sample number

y 0 :

Measured value

y p :

Predicted value

m :

Number of Monte Carlo iterations

S :

Actual random variable considered

K e :

Normalized thermal conductivity of the soil

S r :

Soil saturation

λ water :

Thermal conductivities of water [Wm−1 K−1]

λ solid :

Thermal conductivities of soil solid particles [Wm−1 K−1]

κ :

Empirical parameters

χ,η :

Influencing parameters of thermal conductivity of dry soil

a, b:

Parameters related to dry soil

α :

Reflects the influence of soil type on the Kersten variable

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Funding

Majority of the work presented in this paper was funded by the National Key R&D Program of China (Gran No. 2020YFC1807200), the National Natural Science Foundation of China (Grant No. 41877231, No. 42072299).

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Authors and Affiliations

  1. Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China

    Caijin Wang, Guojun Cai, Xuening Liu & Meng Wu

  2. School of Civil Engineering, Anhui Jianzhu University, Hefei, 230601, China

    Guojun Cai

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  1. Caijin Wang
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Correspondence to Guojun Cai.

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Wang, C., Cai, G., Liu, X. et al. Prediction of soil thermal conductivity based on Intelligent computing model. Heat Mass Transfer 58, 1695–1708 (2022). https://doi.org/10.1007/s00231-022-03209-y

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