Optimizing Suspended Sediment Models: A Novel Expert System with Spatial Probabilities and Isolated Points

Abstract

Predicting suspended sediment concentration (SSC) profiles with high accuracy remains a critical challenge for environmental and engineering applications. This study presents a novel, data-driven expert system that leverages a knowledge-based framework to select optimal SSC models based on diverse flow conditions. The system utilizes model function ranges and spatial relationships between data points as key decision factors. This methodology is applied to study vertical velocity profiles and SSC distribution in steady and uniform river flows. The system systematically extracts and categorizes influencing parameters, generating weighted averages to interpolate and extrapolate profiles where single models exhibit limitations. Two weight calculation methods are implemented: (1) a spatial conditional probability approach utilizing a uniform distribution within control cells, and (2) an isolated point analysis based on distances to cell centers. This approach exhibits some similarities to Voronoi tessellations and associated Laplace and Sibson weights, offering a robust and innovative method for SSC modeling. The proposed expert system empowers hydrologists and engineers by selecting and applying the most suitable SSC models for different scenarios, leading to enhanced prediction accuracy and reliability. This work represents a significant advancement in the field of sediment transport modeling, providing a valuable tool for improved water resource management and environmental protection.