Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility

Qiang Hu, Yuelong Zhu, Hexuan Hu (), Zhuang Guan, Zeyu Qian and Aiming Yang
Additional contact information
Qiang Hu: Hohai University
Yuelong Zhu: Hohai University
Hexuan Hu: Hohai University
Zhuang Guan: Hohai University
Zeyu Qian: Hohai University
Aiming Yang: Hohai University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 1, No 4, 55-73

Abstract: Abstract Identifying flood prone areas is essential for basin management. In this paper, a spatial prediction technology of flood susceptibility based on multiple kernel learning (MKL) is proposed. We establish the flood susceptibility model by using EasyMKL, nonlinear MKL (NLMKL), Representative MKL(RMKL), Generalized MKL(GMKL), support vector machine(SVM) with linear kernel and SVM with Gaussian radial base function(RBF) kernel, The spatial prediction of flood susceptibility in Sanhuajian basin of the Yellow River is carried out. We use MODIS remote sensing images to obtain historical flood inundation sites in the study area. Then, ten flood conditioning factors are used as inputs to the flood susceptibility model. The model performance is evaluated in terms of accuracy (ACC), balanced F Score (F1 score), and areas under the curve (AUC). According to the results, MKL significantly outperforms the SVM adopting single kernel, and NLMKL(ACC=0.833,F1=0.841,AUC=0.889) demonstrates the best comprehensive performance. The flood susceptibility map generated by MODIS remote sensing images and MKL, therefore, can provide effective help for researchers and decision makers in flood management.

Keywords: Flood susceptibility; Multiple kernel learning; Support vector machine; Remote sensing (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-021-03010-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:36:y:2022:i:1:d:10.1007_s11269-021-03010-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-021-03010-2

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().