University of Oulu

Hamid Darabi, Omid Rahmati, Seyed Amir Naghibi, Farnoush Mohammadi, Ebrahim Ahmadisharaf, Zahra Kalantari, Ali Torabi Haghighi, Seyed Masoud Soleimanpour, John P. Tiefenbacher & Dieu Tien Bui (2021) Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood, Geocarto International, DOI:

Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood

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Author: Darabi, Hamid1; Rahmati, Omid2; Naghibi, Seyed Amir3;
Organizations: 1Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland
2Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
3Department of Water Resources Engineering & Center for Advanced Middle Eastern Studies, Lund University, Lund, Sweden
4Faculty of Natural Resources Management, University of Tehran, Karaj, Iran
5DHI, Lakewood, CO, USA
6Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
7Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, Stockholm, Sweden
8Soil Conservation and Watershed Management Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran
9Department of Geography, Texas State University, San Marcos, TX, USA
10Geographic Information System group, Department of Business and IT, University of South-Eastern Norway, Bø i Telemark, Norway
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 6.1 MB)
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Language: English
Published: Informa, 2021
Publish Date: 2021-12-01


In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.

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Series: Geocarto international
ISSN: 1010-6049
ISSN-E: 1752-0762
ISSN-L: 1010-6049
Issue: Online first
DOI: 10.1080/10106049.2021.1920629
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Funding: This work was supported by the Maa- ja vesitekniikan tuki r.y. (MVTT), to which the authors would like to express their deep gratitude.
Copyright information: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.