Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping

This paper presents a comprehensive study focusing on the influence of DEM type and spatial resolution on the accuracy of flood inundation prediction. The research employs a state-of-the-art deep learning method using a 1D convolutional neural network (CNN). The CNN-based method employs training input data in the form of synthetic hydrographs, along with target data represented by water depth obtained utilizing a 2D hydrodynamic model, LISFLOOD-FP. The performance of the trained CNN models is then evaluated and compared with the observed flood event. This study examines the use of digital surface models (DSMs) and digital terrain models (DTMs) derived from a LIDAR-based 1m DTM, with resolutions ranging from 15 to 30 meters. The proposed methodology is implemented and evaluated in a well-established benchmark location in Carlisle, UK. The paper also discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. The study found that DTM performs better than DSM at lower resolutions. Using a 30m DTM improved flood depth prediction accuracy by about 21% during the peak stage. Increasing the resolution to 15m increased RMSE and overlap index by at least 50% and 20% across all flood phases. The study demonstrates that while coarser resolution may impact the accuracy of the CNN model, it remains a viable option for rapid flood prediction compared to hydrodynamic modeling approaches

Flood Damage Estimation

Estimated damage for both deterministic and probabilistic approaches using FDE model developed by Karamouz et al. (2016).<div><br></div><div><br></div><div><br></div><div><br></div><div>Karamouz, M., Fereshtehpour, M., Ahmadvand, F., & Zahmatkesh, Z. (2016). Coastal flood damage estimator: An alternative to FEMA’s HAZUS platform. <i>Journal of Irrigation and Drainage Engineering</i>, <i>142</i>(6), 04016016.<br></div

Deterministic and Probabilistic Flood risk assessment

<div><br><div>Flood modeling, as an important part of coastal hazard assessment, is highly influenced by topography dataset and specifically ground elevation. Lower resolution Digital Elevation Models (DEMs) are usually used because of their availability and less computational burden. However, inherent errors in these DEMs propagate into flood risk analysis through spatial modeling. This study aims to explore the DEM resolution effects on coastal flood risk assessments. For this purpose, deterministic and probabilistic approaches are employed. Flood inundation modeling is carried out using hydrologically connected bathtub method. Given the high resolution Light Detection And Ranging (LiDAR) DEM, different resolution maps are obtained used resampling techniques and incorporated into an error analysis framework along with USGS national elevation dataset (NED) DEMs. The probabilistic framework is developed by simulating the spatial variability of elevation errors compared to LiDAR DEM through a Monte Carlo based method called sequential Gaussian simulation. The proposed methodology is applied to the lower Manhattan in New York City. By integrating the flood model into the developed framework, this approach results in flood inundation probability at each grid cells. In this study, using the concept of accuracy-efficiency tradeoffs, a framework for selecting a suitable spatial resolution for probabilistic flood risk assessment has been suggested. The results show that by exercising a range of options presented in this paper, a broader insight into mapping resolution can be made for making better flood assessment, evacuation zones, and mitigation plans depending upon the data availability in a region for flood preparedness.<br></div></div