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

Author

Listed:
  • Qiang Hu

    (Hohai University)

  • Yuelong Zhu

    (Hohai University)

  • Hexuan Hu

    (Hohai University
    Tibet Agriculture & Animal Husbandry University)

  • Zhuang Guan

    (Hohai University)

  • Zeyu Qian

    (Hohai University)

  • Aiming Yang

    (Hohai University
    Tibet Agriculture & Animal Husbandry University
    Changjiang Survey, Planning, Design and Research Co., Ltd
    Changjiang Spatial Information Technology Engineering Co., Ltd)

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.

Suggested Citation

  • Qiang Hu & Yuelong Zhu & Hexuan Hu & Zhuang Guan & Zeyu Qian & Aiming Yang, 2022. "Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 55-73, January.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:1:d:10.1007_s11269-021-03010-2
    DOI: 10.1007/s11269-021-03010-2
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    References listed on IDEAS

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    1. Peyman Yariyan & Saeid Janizadeh & Tran Phong & Huu Duy Nguyen & Romulus Costache & Hiep Le & Binh Thai Pham & Biswajeet Pradhan & John P. Tiefenbacher, 2020. "Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3037-3053, July.
    2. Saeid Janizadeh & Mehdi Vafakhah & Zoran Kapelan & Naghmeh Mobarghaee Dinan, 2021. "Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4621-4646, October.
    3. Lorena Liuzzo & Vincenzo Sammartano & Gabriele Freni, 2019. "Comparison between Different Distributed Methods for Flood Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3155-3173, July.
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