Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

Accuracy and computational efficiency of 2D urban surface flood modelling based on cellular automata

There is an emerging abundance freely available of high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and Wastewater systems in the UK contain over 700,000 km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required calling for the most computational efficient models possible. Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES, a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy.This is the final version of the article. Available from Elsevier via the DOI in this record.There is an emerging abundance freely available of high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and Wastewater systems in the UK contain over 700,000 km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required calling for the most computational efficient models possible. Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES, a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy

Estimating flood characteristics using geomorphologic flood index with regards to rainfall intensity-duration-frequency-area curves and CADDIES-2D model in three Iranian basins

This is the final version. Available on open access from MDPI via the DOI in this recordThere is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.University of BasilicataCNR-IMAAOpenet TechnologiesRoyal Academy of Engineering (RAE

An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.High accuracy models are required for informed decision making in urban flood management. This paper develops a new holistic framework for using information collected from multiple sources for setting parameters of a 2D flood model. This illustrates the importance of identifying key urban features from the terrain data for capturing high resolution flood processes. A Cellular Automata based model CADDIES was used to simulate surface water flood inundation. Existing reports and flood photos obtained via social media were used to set model parameters and investigate different approaches for representing infiltration and drainage system capacity in urban flood modelling. The results of different approaches to processing terrain datasets indicate that the representation of urban micro-features is critical to the accuracy of modelling results. The constant infiltration approach is better than the rainfall reduction approach in representing soil infiltration and drainage capacity, as it describes the flood recession process better. This study provides an in-depth insight into high resolution flood modelling.This research was partially funded by the British Council through the Global Innovation Initiative (GII206), the UK Engineering and Physical Sciences Research Council under the Building Resilience into Risk Management project (EP/N010329/1), and the SINATRA project of the NERC Flooding From Intense Rainfall programme (NE/K008765/1). The first author was funded by the China Scholarship Council. The authors would also like to thank the UK Environment Agency for the LIDAR datasets, UK Met Office (BADC) for the Radar rainfall data, Ordnance Survey for the Master Maps, and NVIDIA Corporation for the Tesla K20c GPU used in this research

Validating a rapid assessment framework for screening surface water flood risk

This is the final version. Available on open access from Wiley via the DOI in this recordThis research evaluates performance of a rapid assessment framework for screening surface water flood risk in urban catchments. Recent advances in modelling have developed fast and computationally efficient cellular automata frameworks which demonstrate promising utility for increasing available evidence to support surface water management, however, questions remain regarding trade‐offs between accuracy and speed for practical application. This study evaluates performance of a rapid assessment framework by comparing results with outputs from an industry standard hydrodynamic model using a case study of St Neots in Cambridgeshire, UK. Results from the case study show that the rapid assessment framework is able to identify and prioritise areas of flood risk and outputs flood depths which correlate above 97% with the industry standard approach. In theory, this finding supports a simplified representation of catchments using cellular automata, and in practice presents an opportunity to apply the framework to develop evidence to support detailed modelling.This research was funded by the UK Engineering & Physical Sciences Research Council through the Water Informatics Science and Engineering Centre for Doctoral Training (EP/L016214/1) and the Safe & SuRe research fellowship (EP/K006924/1)

Inundation resilience analysis of metro-network from a complex system perspective using the grid hydrodynamic model and FBWM approach : a case study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network

Assessing real options in urban surface water flood risk management under climate change

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordDeveloping an adaptation option is challenging for long-term engineering decisions due to uncertain future climatic conditions; this is especially true for urban flood risk management. This study develops a real options approach to assess adaptation options in urban surface water flood risk management under climate change. This approach is demonstrated using a case study of Waterloo in London, UK, in which three Sustainable Drainage System (SuDS) measures for surface water flood management, i.e., green roof, bio-retention and permeable pavement, are assessed. A trinomial tree model is used to represent the change in rainfall intensity over future horizons (2050 s and 2080 s) with the climate change data from UK Climate Projections 2009. A two-dimensional Cellular Automata-based model CADDIES is used to simulate surface water flooding. The results from the case study indicate that the real options approach is more cost-effective than the fixed adaptation approach. The benefit of real options adaptations is found to be higher with an increasing cost of SuDS measures compared to fixed adaptation. This study provides new evidence on the benefits of real options analysis in urban surface water flood risk management given the uncertainty associated with climate change.This research was partially funded by the British Council through the Global Innovation Initiative (GII206) and the UK Engineering and Physical Sciences Research Council under the Building Resilience into Risk Management project (EP/N010329/1). The corresponding author was funded by the China Scholarship Council