Machine learning with monotonic constraint for geotechnical engineering applications: an example of slope stability prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geotechnical Engineering and Engineering Geology
Link
https://link.springer.com/content/pdf/10.1007/s11440-023-02117-7.pdf
Reference53 articles.
1. Archer NP, Wang S (1993) Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems. Decis Sci 24:60–75. https://doi.org/10.1111/j.1540-5915.1993.tb00462.x
2. Bishop AW (1955) The use of the slip circle in the stability analysis of slopes. Géotechnique 5:7–17. https://doi.org/10.1680/geot.1955.5.1.7
3. Bartley C, Liu W, Reynolds M (2019) Enhanced random forest algorithms for partially monotone ordinal classification. Proc Conf AAAI Artif Intell 33:3224–3231. https://doi.org/10.1609/aaai.v33i01.33013224
4. Bandai T, Ghezzehei TA (2021) Physics-informed neural networks with monotonicity constraints for Richardson-Richards equation: estimation of constitutive relationships and soil water flux density from volumetric water content measurements. Water Resour Res. https://doi.org/10.1029/2020wr027642
5. Chen CC, Li ST (2014) Credit rating with a monotonicity-constrained support vector machine model. Expert Syst Appl 41:7235–7247. https://doi.org/10.1016/j.eswa.2014.05.035
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