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.

翻译：水文和地形时空数据的日益丰富推动了机器学习在快速洪水制图中的广泛应用。然而，数据稀缺性——尤其是高分辨率DEM的缺乏——对数据获取受限地区构成挑战。本文探讨了DEM类型与分辨率对洪水预测精度的影响，采用前沿的深度学习（DL）方法——一维卷积神经网络（1D CNN）。该方法利用合成水文过程线作为训练输入，以LISFLOOD-FP（一种二维水动力模型）输出的水深数据作为目标数据。研究基于1米激光雷达DTM衍生出的数字表面模型（DSM）和数字地形模型（DTM），空间分辨率范围为15至30米。该方法在英国卡莱尔市这一既定基准区域得到应用与评估。随后，通过均方根误差（RMSE）、偏差（Bias）和拟合指数（Fit）等指标，对比分析了各模型与实际洪水事件的预测表现。基于该区域的研究成果，论文进一步探讨了该方法在解决数据稀缺易涝地区（以巴基斯坦为例）挑战中的适用性。结果表明：在洪峰阶段，使用30米DTM的水深预测精度比30米DSM高出约21%，凸显了DTM在低分辨率下的优越性能。将DTM分辨率提升至15米后，所有洪水阶段的RMSE至少增加50%，拟合指数提升20%。研究强调：尽管较粗分辨率的DEM可能影响机器学习模型精度，但仍是快速洪水预测的可行选择；然而，在数据稀缺地区，即使数据分辨率的微小提升也能带来显著附加价值，最终增强洪水风险管理能力。