用隐变量与伴随方程提升城市内涝参数校准效率与精度
Calibration of the underlying surface parameters for urban flood using latent variables and adjoint equation
- 引入机器学习启发的隐变量表征不确定性，兼容物理参数校准
- 通过伴随方程快速获取梯度，最大相对误差仅13.88%
- 适合需要高精度内涝模拟的城市防灾研究者
城市地表参数校准对内涝模拟至关重要。本文在贝叶斯框架下，基于最大似然原则将参数校准问题建模为优化问题。采用城市洪水动力学模型作为代理模型，创新性引入受机器学习启发的隐变量以表征更多不确定性，同时保持与常见物理参数校准的兼容性。为提升优化效率，构建了代理模型的伴随方程以获取梯度信息，并提出参数共享与局部化技术以降低伴随方程计算复杂度。简单案例验证该方法收敛迅速，且对观测时间间隔不敏感。在源自Test 8A的案例中，成功校准城市道路曼宁系数，最大相对误差为13.88%，最小为1.16%。
原文摘要 · Abstract (English)
Calibrating the urban underlying surface parameters is crucial for urban flood simulation. We formulate the parameter calibration problem into an optimization problem within the Bayesian framework using the maximum likelihood principle. We adopt the urban flood dynamical system model as the surrogate model and innovatively introduce latent variables inspired by machine learning to represent more uncertainties, which can also be compatible with common physical parameter calibration. For more efficient optimization, we construct the adjoint equation of the surrogate model to obtain gradient information and propose the parameter sharing technique and the localization technique to reduce the computation complexity of the adjoint equation. A simple case verifies the proposed method can converge quickly and is insensitive to the observation time interval. In the case derived from Test 8A, we calibrate Manning's coefficient of urban roads, with a maximum relative error of 13.88% and a minimum of 1.16%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。