Sigma-pi neural network model for data clustering
- Authors: Zhilov R.A.1
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Affiliations:
- Institute of Applied Mathematics and Automation - branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences
- Issue: Vol 27, No 5 (2025)
- Pages: 34-42
- Section: System analysis, management and information processing, statistics
- Submitted: 13.11.2025
- Published: 20.11.2025
- URL: https://journals.rcsi.science/1991-6639/article/view/351242
- DOI: https://doi.org/10.35330/1991-6639-2025-27-5-34-42
- EDN: https://elibrary.ru/HCZACC
- ID: 351242
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Abstract
Mudflows are some of the most destructive geological phenomena, and their prediction is challenging due to their complexity and the strong nonlinear relationships between the various factors that contribute to their formation. Traditional modeling methods have limitations in their ability to interpret and account for the complex interactions between different factors, and this lead to the need for the development of more advanced approaches.
Aim. The study aims to develop and test a sigma-pi neural network architecture for mudflow clustering based on morphometric and genetic characteristics as well as to identify the key factors and their combinations that contribute to the formation of different mudflow types.
Materials and methods. Cadastral data on mudflows in the southern European part of Russia is used as the initial data. A sigma-pi neural network capable of accounting for both linear features and their second-order interactions is employed for analysis. A silhouette coefficient is used to determine the number of clusters. The results are compared with those obtained using Kohonen's self-organizing maps (SOM).
Results. The model identified three stable clusters corresponding to mud, rock, and mud-rock types of mudflows. Analysis of the significance of features has revealed that the basin area, channel slope, and maximum sediment volume make the greatest contributions to cluster formation, as well as their various pairwise combinations. Comparison with the SOM (self-organizing map) confirmed the improved interpretability of the proposed model and its ability to identify hidden, nonlinear relationships.
Conclusions. The use of sigma-pi neural networks not only improves the accuracy of mudflow clustering, but also ensures the interpretability of the results by analyzing the significance of features and their combinations. This approach is promising for engineering geology and can be used in geoecological monitoring systems and forecasting of hazardous processes.
About the authors
R. A. Zhilov
Institute of Applied Mathematics and Automation - branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences
Author for correspondence.
Email: zhilov91@gmail.com
ORCID iD: 0000-0002-3552-4854
SPIN-code: 9389-6188
Junior Researcher, Neuroinformatics and Machine Learning Department
Russian Federation, 89 A, Shortanov street, Nalchik, 360000, RussiaReferences
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