Procedural generation of flood-sensitive urban layouts

Ahmed Mustafa, Xiao Wei Zhang, Daniel G Aliaga, Martin Bruwier, Gen Nishida, Benjamin Dewals, Sébastian Erpicum, Pierre Archambeau, Michel Pirotton and Jacques Teller
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Daniel G Aliaga: Purdue University, USA
Martin Bruwier: Liège University, Belgium
Gen Nishida: Purdue University, USA

Environment and Planning B, 2020, vol. 47, issue 5, 889-911

Abstract: Aside from modeling geometric shape, three-dimensional (3D) urban procedural modeling has shown its value in understanding, predicting and/or controlling effects of shape on design and urban planning. In this paper, instead of the construction of flood resistant measures, we create a procedural generation system for designing urban layouts that passively reduce water depth during a flooding scenario. Our tool enables exploring designs that passively lower flood depth everywhere or mostly in chosen key areas. Our approach tightly integrates a hydraulic model and a parameterized urban generation system with an optimization engine so as to find the least cost modification to an initial urban layout design. Further, due to the computational cost of a fluid simulation, we train neural networks to assist with accelerating the design process. We have applied our system to several real-world locations and have obtained improved 3D urban models in just a few seconds.

Keywords: Inverse procedural modeling; urban layout; urban flooding; neural network; Markov Chain Monte Carlo (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:47:y:2020:i:5:p:889-911

DOI: 10.1177/2399808318812458

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