Hamid Darabi, Ali Torabi Haghighi, Mohamad Ayob Mohamadi, Mostafa Rashidpour, Alan D. Ziegler, Ali Akbar Hekmatzadeh, Bjørn Kløve; Urban flood risk mapping using data-driven geospatial techniques for a flood-prone case area in Iran. Hydrology Research 1 February 2020; 51 (1): 127–142. doi: https://doi.org/10.2166/nh.2019.090
Urban flood risk mapping using data-driven geospatial techniques for a flood-prone case area in Iran
|Author:||Darabi, Hamid1; Torabi Haghighi, Ali1; Mohamadi, Mohamad Ayob2;|
1Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN-90014 Oulu, Finland
2Department of Watershed Management, Sari Agriculture Science and Natural Resources University, P.O. Box 737, Sari, Iran
3Geography Department, National University of Singapore, Singapore
4Department of Civil & Environmental Engineering, Shiraz University of Technology, Iran
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019121848802
|Publish Date:|| 2019-12-18
In an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city.
|Pages:||127 - 142|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
© 2020 The Authors. This is an Open Access article distributed under the terms of the CreativeCommons Attribution Licence (CC BY 4.0), which permits copying,adaptation and redistribution, provided the original work is properly cited(http://creativecommons.org/licenses/by/4.0/).