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.

翻译：本文系统研究了DEM类型与空间分辨率对洪水淹没预测精度的影响。研究采用基于一维卷积神经网络（CNN）的前沿深度学习方法。该CNN方法以合成水文过程线作为训练输入数据，以利用二维水动力模型LISFLOOD-FP获取的水深作为目标数据。训练后的CNN模型性能通过实测洪水事件进行评估与对比。本研究考察了基于激光雷达1米DTM生成的数字表面模型（DSM）与数字地形模型（DTM），其分辨率范围为15至30米。所提方法在英格兰卡莱尔市一个成熟的基准区域实施并验证。论文还探讨了该方法在数据稀缺的洪水易发区域（以巴基斯坦为例）的应用可行性。研究发现，在较低分辨率下DTM表现优于DSM。使用30米DTM可将洪峰阶段水深预测精度提高约21%。当分辨率提升至15米时，所有洪水阶段的均方根误差（RMSE）与重叠指数至少分别增加50%与20%。研究表明，尽管较低分辨率可能影响CNN模型精度，但相较于水动力建模方法，该方法仍是快速洪水预测的可行方案。