Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Lamane:2025:ijsrc,

• author = "Houda Lamane and Latifa Mouhir and Rachid Moussadek and Bouamar Baghdad and Ozgur Kisi and Ali {El Bilali}",
• title = "Interpreting machine learning models based on {SHAP} values in predicting suspended sediment concentration",
• journal = "International Journal of Sediment Research",
• year = "2025",
• volume = "40",
• number = "1",
• pages = "91--107",
• keywords = "genetic algorithms, genetic programming, Interpretability, Machine learning (ML), Shapley values, Suspended sediment concentration (SSC), Soil erosion, Bouregreg watershed (BW)",
• ISSN = "1001-6279",
• URL = "https://www.sciencedirect.com/science/article/pii/S1001627924001070",
• DOI = "doi:10.1016/j.ijsrc.2024.10.002",
• abstract = "black box' character, and provided a useful source of information for assessing the consequences of SSC on water quality. The SHAP system and exploring other interpretable techniques are recommended to provide further information in future research. In addition, incorporating additional input data could enhance SSC predictions and deepen understanding of sediment transport dynamics",

}

Genetic Programming entries for Houda Lamane Latifa Mouhir Rachid Moussadek Bouamar Baghdad Ozgur Kisi Ali El Bilali

Citations