University of Oulu

Rahmati, O.; Darabi, H.; Haghighi, A.T.; Stefanidis, S.; Kornejady, A.; Nalivan, O.A.; Tien Bui, D. Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network. Water 2019, 11, 2370.

Urban flood hazard modeling using self-organizing map neural network

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Author: Rahmati, Omid1; Darabi, Hamid2; Torabi Haghighi, Ali2;
Organizations: 1Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, 6616936311 Sanandaj, Iran
2Water, Energy and Environmental Engineering Research Unit, University of Oulu, 90570 Oulu, Finland
3Laboratory of Mountainous Water Management and Control, Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
4Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, 4918943464 Gorgan, Iran
5Geographic Information Science Research Group, Ton Duc Thang University, 70000 Ho Chi Minh City, Vietnam
6Faculty of Environment and Labour Safety, Ton Duc Thang University, 70000 Ho Chi Minh City, Vietnam
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.7 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2019
Publish Date: 2019-11-22


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.

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Series: Water
ISSN: 2073-4441
ISSN-E: 2073-4441
ISSN-L: 2073-4441
Volume: 11
Issue: 11
DOI: 10.3390/w11112370
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
Copyright information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (