A novel elemental composition based prediction model for biochar aromaticity derived from machine learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{CAO:2021:AIA,

• author = "Hongliang Cao and Yaime Jefferson Milan and Sohrab Haghighi Mood and Michael Ayiania and Shu Zhang and Xuzhong Gong and Electo Eduardo Silva Lora and Qiaoxia Yuan and Manuel Garcia-Perez",
• title = "A novel elemental composition based prediction model for biochar aromaticity derived from machine learning",
• journal = "Artificial Intelligence in Agriculture",
• year = "2021",
• volume = "5",
• pages = "133--141",
• keywords = "genetic algorithms, genetic programming, Biochar, C aromaticity, Prediction model, Machine learning",
• ISSN = "2589-7217",
• URL = "https://www.sciencedirect.com/science/article/pii/S2589721721000210",
• DOI = "doi:10.1016/j.aiia.2021.06.002",
• abstract = "The measurement of aromaticity in biochars is generally conducted using solid state 13C nuclear magnetic resonance spectroscopy, which is expensive, time-consuming, and only accessible in a small number of research-intensive universities. Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters (e.g. elemental composition). In this research, Genetic Programming (GP), an advanced machine learning method, is used to develop new prediction models. In order to identify and evaluate the performance of prediction models, an experimental data set with 98 biochar samples collected from the literature was used. Due to the benefits of the intelligence iteration and learning of GP algorithm, a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly. The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer, Mazumdar, and Mazumdar-Wang models. In this case, a novel exponential model is proposed for the prediction of biochar aromaticity. The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation",

}

Genetic Programming entries for Hongliang Cao Yaime Jefferson Milan Sohrab Haghighi Mood Michael Ayiania Shu Zhang Xuzhong Gong Electo Eduardo Silva Lora Qiaoxia Yuan Manuel Garcia-Perez

Citations