A Cellular Automata Fast Flood Evaluation (CA‐ffé) Model

Abstract

The simulation speed of two-dimensional hydrodynamic flood models is a limiting factor when catchments are large, a considerable number of simulations is required (e.g., exploratory modeling, Monte-Carlo flood simulations, or predicting probabilistic flood maps), or when there is a need for real-time flood emergency management. Rapid Flood Models (RFMs) that rely only on topographic depressions and the water balance equation have been successfully implemented to predict maximum urban flood inundation depths within seconds to a few minutes. However, the preprocessing step (identification of depressions and their attributes) and the postprocessing step (marking up possible flow paths of flood water in between flooded depressions) of RFMs is time consuming. In this study, we developed a new fast flood inundation model based on the cellular automata (CA) approach. The new model does not require the preprocessing and postprocessing steps of RFMs and therefore can provide more simulation speed. The performance of our new model, referred to as Cellular Automata fast flood evaluation (CA-ffé), was compared to two well-known hydrodynamic flood models (HEC-RAS and TUFLOW) in 20 simulation experiments conducted in five different urban subcatchments. CA-ffé predicted maximum inundation depth with reasonable accuracy in a matter of seconds to a few minutes for a single rainfall event simulation. The CA-ffé model performed exceptionally well in areas with low-lying depressions. However, in areas where floodwaters had higher momentum and velocity, the model usually was not able to estimate inundation depths calculated by HEC-RAS or TUFLOW. CA-ffé's key drawback is also its inability to represent the temporal evolution of flooding and flow velocities. Nevertheless, its ability to provide spatial flood extents and depths in a fraction of the time compared to its hydrodynamic counterparts is a significant advancement toward exploratory approaches for water systems planning, model-based predictive control, and real-time flood management.

Developing an Algorithm for Urban Flood Management With the Aim of Reducing Damage and Costs Using the Concept of Conditional Value at Risk 

“…Among the shortcomings of flow simulation models in the studies conducted by Tavakolifar et al (2021), Abbasizadeh et al (2017) and Jamali et al (2019), one can mention the inability of these models in modeling the effects of permeability of the study area at the flood volume and flow simulation. This makes these models not capable of modeling changes in the flow status and permeability amount of catchment due to the use of BMPs.…”

“…The obtained results showed that the CA method requires less computational time to model the flow than other ones, which use 2D flow motion equations. Using the CA theory, Jamali et al (2019) modelled floods in urban areas and compared the results with those via 2D ones. This comparison illustrated that the proposed CA-based model results in higher flood wave speed than the 2D models.…”

A Hybrid Model for Fast and Probabilistic Urban Pluvial Flood Prediction

“…The issue of computational speed and deterministic nature of the HD models had led researchers to redirect their efforts toward alternatives. One of the promising techniques is the use of data‐driven models (e.g., Berkhahn et al, 2019; Bermúdez et al, 2018, 2019; Jamali et al, 2018; 2019; Jato‐Espino et al, 2018; Jhong et al, 2017; Schultz et al, 2006; Solomatine & Ostfeld, 2008; Vojinovic & Seyoum, 2008; Wolfs & Willems, 2016). Ranging from employing statistical methods to employing artificial intelligence methods, data‐driven models are good at capturing the nonlinear input‐output response surface of the input‐output data sets (Razavi et al, 2012).…”

Efficient Urban Inundation Model for Live Flood Forecasting with Cellular Automata and Motion Cost Fields

“…It can be classified as "finite volume" implementation [18] because it discretizes the spatial domain into control volumes (e.g., regular 2D cells). Previous studies and models on the basis of CA proved its accuracy in modeling hydraulic processes and its computational efficiency [33][34][35][36]. CA models behave like diffusive wave-like models [35] by neglecting the conservation of momentum.…”