  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.
