Designing pluvial flood-safe urban landscapes using differentiable surrogate flood models

Abstract

Integrating pluvial flood-risk management into the early stages of urban planning and design has become a mandatory task for urban planners due to the increasing flood risks caused by climate change. This can be done by optimizing urban layout designs using flood simulations. However, such simulation-driven optimization cannot be easily conducted due to (i) the long computational time for physically-based models to simulate and (ii) the inability to obtain design feedback information without trial and error. To overcome these limitations, this study proposes a gradient descent formulation for pluvial flood-driven urban design by combining flood surrogate models and design objective functions. The proposed method was tested on urban patch data using one-step and iterative gradient-descent processes, showing promising results. The one-step method trained on the original dataset achieved the lowest intersection over union (IoU) ratio of the high-hazard-rating areas between the original inputs and final results, with the median IoU reaching below 0.1. In contrast, the iterative method trained on the expanded dataset achieved the highest IoU value, with the median reaching approximately 0.25. The proposed model in this study is an original contribution that links the areas of flood simulation and urban design in order to create pluvial flood-safe urban layouts.