University of Oulu

Hamid Darabi, Ali Torabi Haghighi, Omid Rahmati, Abolfazl Jalali Shahrood, Sajad Rouzbeh, Biswajeet Pradhan, Dieu Tien Bui, A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation, Journal of Hydrology, Volume 603, Part A, 2021, 126854, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2021.126854

A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

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Author: Darabi, Hamid1; Torabi Haghighi, Ali1; Rahmati, Omid2;
Organizations: 1Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN-90014, Oulu, Finland
2Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
3Department of Watershed Management, Sari Agriculture Science and Natural Resources University, P.O. Box 737, Sari, Iran
4Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia
5Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
6Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia
7GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, N-3800 Bø i Telemark, Norway
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 8.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021110854256
Language: English
Published: Elsevier, 2021
Publish Date: 2021-11-08
Description:

Abstract

In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future studies seeking to predict flood susceptibility in urban areas using hybridized machine learning (ML) models and can also be applied in other urban areas where flood inundation presents a pressing challenge, and there are some problems regarding required model and availability of input data.

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Series: Journal of hydrology
ISSN: 0022-1694
ISSN-E: 1879-2707
ISSN-L: 0022-1694
Volume: 603
Issue: Part A
Article number: 126854
DOI: 10.1016/j.jhydrol.2021.126854
OADOI: https://oadoi.org/10.1016/j.jhydrol.2021.126854
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
Subjects:
GIS
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 and authors would like to thank Sari municipality and regional water company of Mazandaran for providing relevant data.
Copyright information: © 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/