Physics-guided genetic programming for predicting field-monitored suction variation with effects of vegetation and atmosphere

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@Article{CHENG:2023:enggeo,

• author = "Zhi-Liang Cheng and K. K. Pabodha M. Kannangara and Li-Jun Su and Wan-Huan Zhou and Chen Tian",
• title = "Physics-guided genetic programming for predicting field-monitored suction variation with effects of vegetation and atmosphere",
• journal = "Engineering Geology",
• volume = "315",
• pages = "107031",
• year = "2023",
• ISSN = "0013-7952",
• DOI = "doi:10.1016/j.enggeo.2023.107031",
• URL = "https://www.sciencedirect.com/science/article/pii/S0013795223000480",
• keywords = "genetic algorithms, genetic programming, Field-monitored soil suction, Physics-guided genetic programming, Performance evaluation, Global sensitivity analysis, Uncertainty analysis",
• abstract = "The complicated interactions among shallow soil, vegetation, and atmospheric parameters make the precise prediction of field-monitored soil suction under natural conditions challenging. This study integrated an analytical solution with a genetic programming (GP) model in proposing a physics-guided GP method for better calculation and prediction of field-monitored matric suction in a shallow soil layer. Model development and analysis involved 3987 collected data values for soil suction as well as atmospheric and tree-related parameters from a field monitoring site. Natural algorithm values of transpiration rates obtained by back-calculation were simulated with GP using easily obtained parameters. Global sensitivity analysis demonstrated that the tree canopy-related parameter was the most important for transpiration rate. It was indicated that the proposed physics-guided GP method greatly improved calculation accuracy and, as a result, demonstrated a better performance and was more reliable than the individual GP method in calculating field-monitored suction. The proposed physics-guided GP method was also validated as more stable and reliable due to its smaller uncertainty and higher confidence level compared to the individual GP method based on quantile regression uncertainty analysis",

}

Genetic Programming entries for Zhi-Liang Cheng K K Pabodha M Kannangara Li-Jun Su Wan-Huan (Hanna) Zhou Chen Tian

Citations