Journal
JOURNAL OF HYDROLOGY
Volume 601, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jhydrol.2021.126684
Keywords
Flood inundation; Cities; GIS; Deep-learning; Predictors
Funding
- Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM)
- Project of Environmental Business Big Data Platform and Center Construction - Ministry of Science and ICT
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This study evaluated the capability of convolutional neural network (NNETC) and recurrent neural network (NNETR) models for flood hazard mapping in urban areas. The NNETC model showed slightly better prediction performance than the NNETR model, with terrain ruggedness index identified as the most important predictor.
Identification of flood-prone sites in urban environments is necessary, but there is insufficient hydraulic information and time series data on surface runoff. To date, several attempts have been made to apply deep-learning models for flood hazard mapping in urban areas. This study evaluated the capability of convolutional neural network (NNETC) and recurrent neural network (NNETR) models for flood hazard mapping. A flood-inundation inventory (including 295 flooded sites) was used as the response variable and 10 flood-affecting factors were considered as the predictor variables. Flooded sites were then spatially randomly split in a 70:30 ratio for building flood models and for validation purposes. The prediction quality of the models was validated using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The validation results indicated that prediction performance of the NNETC model (AUC = 84%, RMSE = 0.163) was slightly better than that of the NNETR model (AUC = 82%, RMSE = 0.186). Both models indicated that terrain ruggedness index was the most important predictor, followed by slope and elevation. Although the model output had a relative error of up to 20% (based on AUC), this modeling approach could still be used as a reliable and rapid tool to generate a flood hazard map for urban areas, provided that a flood inundation inventory is available.
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