水利学报2026，Vol.57Issue(6)：809-820,12.DOI:10.3724/j.slxb.20250419
人工智能技术(AI)在水沙数学模型中的应用
Application of artificial intelligence in mathematical models of sediment transport
摘要
Abstract
Mathematical models of sediment transport,grounded in physical governing equations,are essential tools for understanding sediment transport processes and supporting basin management and hydraulic engineering.How-ever,conventional numerical methods are often constrained by computational efficiency and thus struggle to meet the demands of emerging scenarios such as digital twins for real-time,high-efficiency prediction and intelligent decision-making.To address this issue,this study reviews three technical routes-data assimilation,data-driven neural net-works,and physics-informed neural networks-within an analytical framework that spans input boundaries and param-eters,governing equations,and model outputs.The results show that:(1)data assimilation can effectively suppress the propagation of errors in initial and boundary conditions,enhancing forecast stability and accuracy;(2)data-driven neural networks can markedly reduce computation time and are suitable for short-term,rapid prediction tasks;and(3)physics-informed neural networks demonstrate better physical consistency under data-scarce condi-tions but face challenges in convergence and accuracy when addressing high-dimensional,multi-scale problems.Based on these findings,this paper proposes an integrated approach oriented to engineering applications,which cen-ters on hybrid physics-data modeling combined with online correction through data assimilation,aiming to balance efficiency and accuracy and to provide a reference for real-time updating and intelligent decision-making in digital-twin sediment-water systems.关键词
水沙数学模型/人工智能/数据同化/数据驱动神经网络/物理信息神经网络Key words
mathematical model of sediment transport/Artificial Intelligence/data assimilation/data-driven neural network/physics-informed neural network分类
天文与地球科学引用本文复制引用
方红卫,张文俊..人工智能技术(AI)在水沙数学模型中的应用[J].水利学报,2026,57(6):809-820,12.基金项目
国家自然科学基金重大项目(52595701) （52595701）
国家重点研发计划项目(2022YFC3201800) （2022YFC3201800）
深圳市杰出人才支持计划项目(202109) （202109）
