Abstract
Hydraulic conductivity is one of the important parameters to simulate water flow in porous environments that is suffer from uncertainty due to the different estimating approaches such as field and laboratory tests. Recently, artificial intelligence models have been applied to estimate hydrogeological parameters based on the available data. In this study, three individual artificial intelligence models including Larsen Fuzzy Logic (LFL), Least Square Support Vector Machine (LSSVM) and Hybrid Wavelet-Artificial Neural Network (WANN) were adopted to estimate hydraulic conductivity in Tabriz Urban Train based on grain size data. For optimal application of the advantages of the individual models, their output was used as the input of a nonlinear combiner. Comparison of the prediction results of this multiple model called Supervised Committee Machine Artificial Intelligence (SCMAI) with the individual models showed a 26% increase in Determination Coefficient (R2) and a 36% decrease in Root Mean Squared Error (RMSE) compared to the most accurate individual artificial intelligence model, i.e., WANN. The results showed that the SCMAI model has a better performance in predicting hydraulic conductivity compared to the individual models and the superiority of the SCMAI model is due to using the type of individual models and the use of special advantages and strengths of each of these models.
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Data availability
The data that support the findings of this study areavailable from the corresponding author, upon reasonable request.
Abbreviations
- AI:
-
Artificial Intelligence
- SVM:
-
Support Vector Machine
- LSSVM:
-
Least Square Support Vector Machine
- ANN:
-
Artificial Neural Network
- WANN:
-
Hybrid Wavelet –Artificial Neural Network
- FL:
-
Fuzzy Logic
- LFL:
-
Larsen Fuzzy Logic
- MFL:
-
Mamdani Fuzzy Logic
- SFL:
-
Sugeno Fuzzy Logic
- KKT:
-
Karush Kuhn Tucker
- GLUE ANN:
-
Generalized Likelihood Uncertainty Estimation ANN
- LM:
-
Levenberg Marquardt
- M5Tree:
-
M5 model Tree
- FCM:
-
Fuzzy C-Means
- NF:
-
Neuro Fuzzy
- SCMAI:
-
Supervised Committee Machine Artificial Intelligence
- RMSE:
-
Root Mean Squared Error
- DC (R2):
-
Determination Coefficient
- MLP:
-
Multi-Layer Perceptron
- RBF:
-
Radial Basis Function
- Poly:
-
Polynomial
- Lin:
-
Linear
- SICM:
-
Supervised Intelligent Committee Machine
- K:
-
Hydraulic Conductivity
- MARS:
-
Multivariate Adaptive Regression Splines
- ELM:
-
Extreme Learning Machine
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Ramin Vafaei Poursorkhabi and Mohammad Khalili-Maleki: Conceptualization, Writing- original draft, Software, Formal analysis, Visualization.
Ata Allah Nadiri: Formal analysis; Writing- original draft, Visualization: Rouzbeh Dabiri: Writing, Review and editing, Supervision.
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Khalili-Maleki, M., Poursorkhabi, R.V., Nadiri, A.A. et al. Prediction of hydraulic conductivity based on the soil grain size using supervised committee machine artificial intelligence. Earth Sci Inform 15, 2571–2583 (2022). https://doi.org/10.1007/s12145-022-00848-x
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DOI: https://doi.org/10.1007/s12145-022-00848-x