Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network

Hadian, Sanaz; Afzalimehr, Hossein; Soltani, Negar; Tabarestani, Ehsan Shahiri; Karakouzian, Moses; Nazari-Sharabian, Mohammad

doi:10.3390/w14111721

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Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network

Sanaz Hadian

¹,

Hossein Afzalimehr

²,

Negar Soltani

³,

Ehsan Shahiri Tabarestani

¹,

Moses Karakouzian

⁴

and

Mohammad Nazari-Sharabian

^4,*

Department of Civil Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran

Natural Disasters Prevention Research Center, School of Civil Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran

Department of Water Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran

⁴

Department of Civil and Environmental Engineering and Construction, University of Nevada Las Vegas, Las Vegas, NV 89154, USA

Author to whom correspondence should be addressed.

Academic Editor: Gwo-Fong Lin

Water 2022, 14(11), 1721; https://doi.org/10.3390/w14111721

Received: 25 April 2022 / Revised: 19 May 2022 / Accepted: 25 May 2022 / Published: 27 May 2022

(This article belongs to the Section Hydrology)

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Abstract

Golestan Province is one of the most vulnerable areas to catastrophic flood events in Iran. The flood severity in this region has grown dramatically during the last decades, demanding a major investigation. Accordingly, an authentic map providing detailed information on floods is required to reduce future flood disasters. Three ensemble models produced by the combination of Evaluation Based on Distance from Average Solution (EDAS) and Multilayer Perceptron Neural Network (MLP) with Frequency Ratio (FR), and Weights of Evidence (WOE) are used to quantify the map flood susceptibility in Golestan Province, in the north of Iran. Ten flood effective criteria, namely altitude, slope degree, slope aspect, plan curvature, distance from rivers, Topographic Wetness Index (TWI), rainfall, soil type, geology, and land use, are considered for the modeling process. The flood zonation maps are validated by the receiver operating curve (ROC). The results show that the most precise model is MLP-FR (AUROC = 0.912), followed by EDAS-FR-AHP (AUROC = 0.875), and EDAS-WOE-AHP (AUROC = 0.845). The high accuracies of all methods applied to illustrate their capability in predicting flood susceptibility in future studies. View Full-Text

Keywords: EDAS method; flood influential factors; flood zonation map; MLP method; ROC curve

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MDPI and ACS Style

Hadian, S.; Afzalimehr, H.; Soltani, N.; Tabarestani, E.S.; Karakouzian, M.; Nazari-Sharabian, M. Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network. Water 2022, 14, 1721. https://doi.org/10.3390/w14111721

AMA Style

Hadian S, Afzalimehr H, Soltani N, Tabarestani ES, Karakouzian M, Nazari-Sharabian M. Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network. Water. 2022; 14(11):1721. https://doi.org/10.3390/w14111721

Chicago/Turabian Style

Hadian, Sanaz, Hossein Afzalimehr, Negar Soltani, Ehsan Shahiri Tabarestani, Moses Karakouzian, and Mohammad Nazari-Sharabian. 2022. "Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network" Water 14, no. 11: 1721. https://doi.org/10.3390/w14111721

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Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network

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