替代模型
雨水管理模型
马尔科夫蒙特卡洛
计算机科学
数学优化
贝叶斯推理
大洪水
高斯过程
不确定度量化
贝叶斯优化
贝叶斯定理
贝叶斯概率
高斯分布
机器学习
人工智能
数学
雨水
地表径流
物理
量子力学
生态学
哲学
神学
生物
作者
Ahad Hasan Tanim,Corinne Smith-Lewis,Austin Downey,Jasim Imran,Erfan Goharian
标识
DOI:10.1016/j.envsoft.2024.106122
摘要
Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. This study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM , specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.
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