Evaluation of Parametric and Nonparametric Machine‐Learning Techniques for Prediction of Saturated and Near‐Saturated Hydraulic Conductivity
The SWLM showed better performance than Lasso in the testing phase for log(Ks) and log(K10) prediction, with RMSE values of 0.666 and 0.551 cm d−1 and R2 of 0.26 and 0.65. Nonparametric supervised machine learning methods trained and tested with a similar data set significantly improved the accuracy...
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| Định dạng: | BB |
| Ngôn ngữ: | English |
| Thông tin xuất bản: |
2020
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| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/9741 |
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| Tóm tắt: | The SWLM showed better performance than Lasso in the testing phase for log(Ks) and log(K10) prediction, with RMSE values of 0.666 and 0.551 cm d−1 and R2 of 0.26 and 0.65. Nonparametric supervised machine learning methods trained and tested with a similar data set significantly improved the accuracy of Ks prediction, with R2 of 0.52, 0.36, and 0.53 for Gaussian process regression (GPR), support vector machine (SVM), and ensemble (ENS) methods in the testing stage. These methods also described 74.9, 66.7, and 72.5% of the variation of log(K10). Bootstrapping validated the strong performance of nonparametric techniques. The feature selection capability of GPR determined that instead of using a model with all predictors, HOR, silt, θpF1, and θpF3 are sufficient for the prediction of log(Ks), while HOR, silt, and OM can predict log(K10) as accurate as the comprehensive model with all variables. |
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