Applying clustered artificial neural networks to enhance contaminant diffusion prediction in geotechnical engineering.

ABSTRACT: This paper introduces a novel approach using Clustered Artificial Neural Networks (CLANN) to address the challenge of developing predictive models for multimodal dataset with extreme parameter values. The CLANN method strategically decomposes the dataset, derived from Finite Element Analysis (FEA), into clusters, each representing distinct diffusion behaviors, and applies specialized neural networks within these clusters. The CLANN model was rigorously evaluated and demonstrated superior accuracy and consistency compared to traditional methods such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy expert systems. While these conventional models struggled to capture the full range of diffusion dynamics, particularly under extreme conditions, CLANN consistently provided predicti

SUBMITTER: Alsamia S 

PROVIDER: S-EPMC11579427 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature