Abstract
Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management.
Highlights
Urban environments are vulnerable to flood damages due to the density of economic and social assets and amount of infrastructure, and there is increasing attention to implementing flood risk reduction measures
Flood inundation probability refers to the probability of the flood occurrence of each pixel over the city which is extracted based on observation flood data and conditioning factors using the self-organizing map neural network (SOMN) model in the current study
In order to simplify the interpretation of the results, the flood hazard map was reclassified into five classes: very low (0–0.2), low (0.2–0.4), medium (0.4–0.6), high (0.6–0.8), and very high (0.8–1)
Summary
Urban environments are vulnerable to flood damages due to the density of economic and social assets and amount of infrastructure, and there is increasing attention to implementing flood risk reduction measures. Instead, growing attention has been given to the spatial prediction of flood risk. Future flood consequences can be limited through risk assessment and flood management measures such as changes in building codes and land uses, improved flood defenses, selective relocation of vulnerable. Flood inundation models play a central role in prediction planning and the implementation of these measures in high-risk zones [3]. Complexities in the urban areas and their drainage infrastructures have an inherent influence on surface runoff and flood inundation, which poses challenges for modeling urban flood risk [2,4]
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