Enhancing solids deposit prediction in gully pots with explainable hybrid models: A review

Abstract

Urban flooding has made it necessary to gain a better understanding of how well gully pots perform when overwhelmed by solids deposition due to various climatic and anthropogenic variables. This study investigates solids deposition in gully pots through the review of eight models, comprising four deterministic models, two hybrid models, a statistical model, and a conceptual model, representing a wide spectrum of solid depositional processes. Traditional models understand and manage the impact of climatic and anthropogenic variables on solid deposition but they are prone to uncertainties due to inadequate handling of complex and non-linear variables, restricted applicability, inflexibility and data bias. Hybrid models which integrate traditional models with data-driven approaches have proved to improve predictions and guarantee the development of uncertainty-proof models. Despite their effectiveness, hybrid models lack explainability. Hence, this study presents the significance of eXplainable Artificial Intelligence (XAI) tools in addressing the challenges associated with hybrid models. Finally, crossovers between various models and a representative workflow for the approach to solids deposition modelling in gully pots is suggested. The paper concludes that the application of explainable hybrid modeling can serve as a valuable tool for gully pot management as it can address key limitations present in existing models. HIGHLIGHTS Existing models are presented and discussed.; Integrating data-driven and traditional models enhances performance.; Explainability could pose a challenge to adopting hybrids.; A review study is conducted on crossovers between different models to explore their limitations and propose potential improvement.; A workflow is developed to address the challenges associated with the implementation of explainable hybrids for prediction.;

Keywords

• explainable hybrid models
• gully pots
• machine learning
• solids deposition prediction