Redirigiendo al acceso original de articulo en 21 segundos...
Inicio  /  Water  /  Vol: 10 Par: 12 (2018)  /  Artículo
ARTÍCULO
TITULO

A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

Mahmood Mahmoodian    
Jairo Arturo Torres-Matallana    
Ulrich Leopold    
Georges Schutz and Francois H. L. R. Clemens    

Resumen

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).

 Artículos similares

       
 
Pablo Moscato, Mohammad Nazmul Haque, Kevin Huang, Julia Sloan and Jonathon Corrales de Oliveira    
In the field of Artificial Intelligence (AI) and Machine Learning (ML), a common objective is the approximation of unknown target functions y=f(x)" role="presentation">??=??(??)y=f(x) y = f ( x ) using limited instances S=(x(i),y(i))" role="presentation... ver más
Revista: Algorithms

 
Yi Liu, Jiang Chen, Jinxin Cheng and Hang Xiang    
The complicated flow conditions and massive design parameters bring two main difficulties to the aerodynamic optimization of axial compressors: expensive evaluations and numerous optimization variables. To address these challenges, this paper establishes... ver más
Revista: Aerospace

 
Xiaojing Wu, Zijun Zuo and Long Ma    
The surrogate-assisted optimization (SAO) process can utilize the knowledge contained in the surrogate model to accelerate the aerodynamic optimization process. The use of this knowledge can be regarded as the primary form of intelligent optimization des... ver más
Revista: Aerospace

 
José Felix Zapata Usandivaras, Annafederica Urbano, Michael Bauerheim and Bénédicte Cuenot    
Improving the predictive capabilities of reduced-order models for the design of injector and chamber elements of rocket engines could greatly improve the quality of early rocket chamber designs. In the present work, we propose an innovative methodology t... ver más
Revista: Aerospace

 
Aikaterini P. Kyprioti, Alexandros A. Taflanidis, Norberto C. Nadal-Caraballo, Madison C. Yawn and Luke A. Aucoin    
Surrogate models, also referenced as metamodels, have emerged as attractive data-driven, predictive models for storm surge estimation. They are calibrated based on an existing database of synthetic storm simulations and can provide fast-to-compute approx... ver más