[1] Feyisa G L, Meilby H, Fensholt R, et al. Automated water extraction index: A new technique for surface water mapping using landsat imagery$$J$$. Remote Sensing of Environment, 2014, 140: 23-35.

[2] Bond N R, Lake P S, Arthington A H. The impacts of drought on freshwater ecosystems: An Australian perspective$$J$$. Hydrobiologia, 2008, 600: 3-16.

[3] Kondo H, Seo N, Yasuda T, et al. Post-flood-infectious diseases in Mozambique$$J$$. Prehospital and Disaster Medicine, 2002, 17(3): 126-133.

[4] Li K Z, Wu S H, Dai E F, et al. Flood loss analysis and quantitative risk assessment in China$$J$$. Natural Hazards, 2012, 63(2): 737-760.

[5] Lazaro T R. Urban Hydrology: A Multidisciplinary Perspective$$M$$. Ann Arbor Science Publishers, 1979.

[11] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation$$J$$. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.

[12] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs$$J$$. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.

[13] Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs$$J/OL$$. ArXiv, 2014, 1412:7062$$2023-8-18$$.https://arxiv.org/pdf/1412.7062.pdf.

[14] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs$$J$$. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2016, 40(4): 834-848.

[16] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation$$C$$// Inter. Conf. on Medical Image Computing and Computer-Assisted Intervention, 2015: 234-241.

[17] Li J T, Xu M L, Xiu H L. U-net network for building information extraction of remote-sensing imagery$$J$$. Inter. J. of Online and Biomedical Engineering, 2018, 14(12): 179-190.

[18] Shamsolmoali P, Zareapoor M, Wang R L, et al. A novel deep structure U-Net for sea-land segmentation in remote sensing images$$J$$. IEEE J. of Sel. Topics in Applied Earth Observations and Remote Sensing, 2019, 12(9): 3219-3232.

[19] Li R, Zheng S Y, Duan C X, et al. Multistage attention ResU-Net for semantic segmentation of fine-resolution remote sensing images$$J$$. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 8009205.

[21] Wang G J, Wu M J, Wei X K, et al. Water identification from high-resolution remote sensing images based on multidimensional densely connected convolutional neural networks$$J$$. Remote Sensing, 2020, 12(5): 795-795.

[25] Hu J, Shen L, Sun G. Squeeze-and-excitation networks$$C$$// Proc. of the 2018 IEEE Conf. on Computer Vision and Pattern Recognition, 2018: 7132-7141.

[26] Wang Q, Wu B, Zhu P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks$$C$$// Proc. of the 2020 IEEE Conf. on Computer Vision and Pattern Recognition, 2020: 11531-11539.

[27] Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module$$C$$// Proc. of the 15th European Conf. on Computer Vision, 2018: 3-19.