🤖 AI Summary
This study investigates how digital elevation model (DEM) type (digital terrain model, DTM vs. digital surface model, DSM) and spatial resolution affect the accuracy of deep learning–based flood inundation prediction—particularly in data-scarce regions where optimal DEM selection remains unresolved.
Method: Using the Carlisle benchmark catchment in the UK, we developed a 1D convolutional neural network (CNN) trained on synthetic hydrographs and supervised by high-fidelity LISFLOOD-FP–simulated water depths to systematically evaluate multiple open-source DEMs.
Contribution/Results: We quantitatively demonstrate that a 30 m DTM improves prediction accuracy by 21% over a 30 m DSM. Counterintuitively, increasing DTM resolution from 30 m to 15 m degrades performance—raising RMSE by 50%—challenging the conventional “higher resolution is better” assumption. The 30 m DTM achieves superior generalization and is validated for rapid, low-cost flood mapping in data-poor regions such as Pakistan, offering a robust methodological and practical framework for high-accuracy, resource-efficient flood risk assessment.
📝 Abstract
The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in an established benchmark, the city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management.
